{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5ae13bf-4fcc-4686-806f-c7ff9d4886a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eae1369f-6a0f-43c5-a99a-2a159a2758cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7d9cfe2d-9be5-4777-8a8a-430473407c15",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import yfinance as yf\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "#from lightgbm import LGBMClassifier\n",
    "from xgboost import XGBClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "def calculate_metrics(returns, confidence_level=0.95):\n",
    "    \"\"\"Calculate comprehensive risk metrics including CVaR\"\"\"\n",
    "    returns_array = np.array(returns)\n",
    "    \n",
    "    # Calculate VaR and CVaR\n",
    "    var_cutoff = np.percentile(returns_array, (1 - confidence_level) * 100)\n",
    "    cvar = returns_array[returns_array <= var_cutoff].mean()\n",
    "    \n",
    "    # Calculate Sortino\n",
    "    excess_returns = returns_array - 0.04/252  # Daily risk-free rate\n",
    "    downside_returns = excess_returns[excess_returns < 0]\n",
    "    downside_std = np.sqrt(np.mean(downside_returns ** 2)) if len(downside_returns) > 0 else 0\n",
    "    sortino = np.sqrt(252) * excess_returns.mean() / downside_std if downside_std != 0 else 0\n",
    "    \n",
    "    # Calculate drawdown\n",
    "    cum_returns = (1 + returns_array).cumprod()\n",
    "    running_max = np.maximum.accumulate(cum_returns)\n",
    "    drawdowns = (cum_returns - running_max) / running_max\n",
    "    max_drawdown = drawdowns.min()\n",
    "    \n",
    "    return {\n",
    "        'CVaR_95': cvar,\n",
    "        'VaR_95': var_cutoff,\n",
    "        'Sortino': sortino,\n",
    "        'MaxDrawdown': max_drawdown,\n",
    "        'Annual_Return': returns_array.mean() * 252\n",
    "    }\n",
    "\n",
    "def prepare_features(data, enhanced=True):\n",
    "    \"\"\"Prepare features with option for enhanced technical indicators\"\"\"\n",
    "    df = data.copy()\n",
    "    \n",
    "    # Basic OHLC features\n",
    "    df['Returns'] = df['Close'].pct_change()\n",
    "    df['Range'] = (df['High'] - df['Low']) / df['Close']\n",
    "    df['Gap'] = df['Open'] - df['Close'].shift(1)\n",
    "    \n",
    " \n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13c64a38-76a8-4f74-bebe-81599b547b2f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def backtest_buy_and_hold(prices, initial_capital=10000):\n",
    "    \"\"\"Backtest a simple buy-and-hold strategy\"\"\"\n",
    "    portfolio_value = [initial_capital]\n",
    "    \n",
    "    # Calculate returns for each day\n",
    "    for i in range(1, len(prices)):\n",
    "        daily_return = (prices.iloc[i] / prices.iloc[i-1]) - 1\n",
    "        portfolio_value.append(\n",
    "            portfolio_value[-1] * (1 + daily_return)\n",
    "        )\n",
    "    \n",
    "    portfolio_values = pd.Series(portfolio_value, index=prices.index)\n",
    "    returns = portfolio_values.pct_change().dropna()\n",
    "    \n",
    "    metrics = calculate_metrics(returns)\n",
    "    metrics.update({\n",
    "        'Total_Return': (portfolio_value[-1] / initial_capital - 1) * 100,\n",
    "        'Trades': 1  # Only one trade - buy at start and hold\n",
    "    })\n",
    "    \n",
    "    return portfolio_values, metrics\n",
    "\n",
    "def backtest_strategy(predictions, prices, initial_capital=10000):\n",
    "    \"\"\"Backtest strategy with realistic transaction costs\"\"\"\n",
    "    portfolio_value = [initial_capital]\n",
    "    position = 0\n",
    "    trades = 0\n",
    "    \n",
    "    # Transaction costs:\n",
    "    pip_value = 0.0001  # Value of one pip\n",
    "    spread_pips = 2  # Average spread in pips\n",
    "    commission_pips = 3  # Commission in pips per round trip\n",
    "    total_cost_pips = spread_pips + commission_pips  # Total cost in pips\n",
    "    transaction_cost = total_cost_pips * pip_value  # Convert to percentage (0.05%)\n",
    "    \n",
    "    for i in range(1, len(prices)):\n",
    "        daily_return = (prices.iloc[i] / prices.iloc[i-1]) - 1\n",
    "        new_position = 1 if predictions[i-1] == 1 else -1\n",
    "        \n",
    "        if new_position != position:\n",
    "            # Apply round-trip transaction costs\n",
    "            portfolio_value[-1] *= (1 - transaction_cost)\n",
    "            trades += 1\n",
    "        \n",
    "        portfolio_value.append(\n",
    "            portfolio_value[-1] * (1 + new_position * daily_return)\n",
    "        )\n",
    "        position = new_position\n",
    "    \n",
    "    portfolio_values = pd.Series(portfolio_value, index=prices.index)\n",
    "    returns = portfolio_values.pct_change().dropna()\n",
    "    \n",
    "    metrics = calculate_metrics(returns)\n",
    "    metrics.update({\n",
    "        'Total_Return': (portfolio_values[-1] / initial_capital - 1) * 100,\n",
    "        'Trades': trades\n",
    "    })\n",
    "    \n",
    "    return portfolio_values, metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "859460ca-aacf-4722-8ee0-f7e0860813f9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmark...\n",
      "Total Return: 4.10%\n",
      "Sortino: 2.52\n",
      "CVaR@95%: -0.56%\n",
      "Max Drawdown: -1.79%\n",
      "Number of Trades: 1\n",
      "\n",
      "Running backtests with transaction costs of 5 pips per round trip (2 spread + 3 commission)\n",
      "\n",
      "Training Logistic_Base...\n",
      "Accuracy: 0.6154\n",
      "Total Return: 9.24%\n",
      "Sortino: 7.68\n",
      "CVaR@95%: -0.46%\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training DecisionTree_Base...\n",
      "Accuracy: 0.5846\n",
      "Total Return: 1.83%\n",
      "Sortino: 0.61\n",
      "CVaR@95%: -0.70%\n",
      "Max Drawdown: -2.81%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training RandomForest_Base...\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.11%\n",
      "Sortino: -0.67\n",
      "CVaR@95%: -0.75%\n",
      "Max Drawdown: -3.45%\n",
      "Number of Trades: 14\n",
      "\n",
      "Training GradientBoosting_Base...\n",
      "Accuracy: 0.5385\n",
      "Total Return: 1.64%\n",
      "Sortino: 0.48\n",
      "CVaR@95%: -0.75%\n",
      "Max Drawdown: -2.67%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training Neural_Base...\n",
      "Accuracy: 0.7077\n",
      "Total Return: 11.54%\n",
      "Sortino: 10.36\n",
      "CVaR@95%: -0.41%\n",
      "Max Drawdown: -1.06%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training AdaBoost_Base...\n",
      "Accuracy: 0.5077\n",
      "Total Return: 0.61%\n",
      "Sortino: -0.20\n",
      "CVaR@95%: -0.77%\n",
      "Max Drawdown: -2.67%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training XGBoost_Base...\n",
      "Accuracy: 0.4769\n",
      "Total Return: -2.01%\n",
      "Sortino: -1.88\n",
      "CVaR@95%: -0.74%\n",
      "Max Drawdown: -3.72%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training Logistic_Enhanced...\n",
      "Accuracy: 0.4308\n",
      "Total Return: -3.16%\n",
      "Sortino: -2.49\n",
      "CVaR@95%: -0.81%\n",
      "Max Drawdown: -4.27%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training DecisionTree_Enhanced...\n",
      "Accuracy: 0.5077\n",
      "Total Return: -2.22%\n",
      "Sortino: -1.91\n",
      "CVaR@95%: -0.78%\n",
      "Max Drawdown: -3.65%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training RandomForest_Enhanced...\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.54%\n",
      "Sortino: -0.93\n",
      "CVaR@95%: -0.75%\n",
      "Max Drawdown: -3.87%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training GradientBoosting_Enhanced...\n",
      "Accuracy: 0.5385\n",
      "Total Return: 1.68%\n",
      "Sortino: 0.50\n",
      "CVaR@95%: -0.75%\n",
      "Max Drawdown: -2.99%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training Neural_Enhanced...\n",
      "Accuracy: 0.6769\n",
      "Total Return: 9.10%\n",
      "Sortino: 8.21\n",
      "CVaR@95%: -0.42%\n",
      "Max Drawdown: -1.06%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training AdaBoost_Enhanced...\n",
      "Accuracy: 0.5231\n",
      "Total Return: -0.01%\n",
      "Sortino: -0.59\n",
      "CVaR@95%: -0.77%\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training XGBoost_Enhanced...\n",
      "Accuracy: 0.4769\n",
      "Total Return: -1.36%\n",
      "Sortino: -1.48\n",
      "CVaR@95%: -0.76%\n",
      "Max Drawdown: -4.35%\n",
      "Number of Trades: 20\n"
     ]
    },
    {
     "data": {
      "image/png": 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XlxcLFy7MUr7kVtSrV4/u3bszatQoTp06RatWrbC3t+fvv//GycmJF154geDgYEaOHMmbb77J4cOHad68OZ6enpw6dYqNGzfi7OzMiBEjcjxGSEgI33//PZ07d6Zy5coMGDDAXH989+7dzJgxA8MwaN++fb5c582bN/PMM8/wxBNPcOzYMd58802KFStGv379gLtz3QGmTp3Kb7/9RsuWLQkKCiIpKYkZM2YAmN9zuX1vioiIiIjIvaXEuYiISCHg4ODAqlWrePPNN5kwYQJnzpyhWLFivPzyyxaJSisrKxYsWMCQIUMYP348KSkp1K1bl8WLF1OmTBmLMcuVK8fWrVt59913eeuttzh9+jQeHh6ULl3a4maOee2pp54iKCiI8ePH8+yzz3L58mV8fX2pXLlylhtO3qqhQ4fi6+vLRx99xOzZs3F0dKRhw4aMGTOG0qVL580J3ICtrS0LFy7kxRdf5Nlnn8XGxoYmTZqwYsUKgoKCbnvcqKgoqlSpwvTp04mKisLR0ZFy5crxxhtvmPsMHTqUcuXKMXnyZL777juSk5Px9/enevXqPPfcczc9RqtWrdi5cycTJ05k6tSpHDt2DCsrK0JCQmjevDkvvPCCxbHu5XWePn06s2bNokuXLiQnJxMREcHkyZPNddLv1nWvXLkyy5YtY9iwYcTGxuLi4kKFChVYsGABTZs2BXL/3hQRERERkXvLZBiGkd9BiIiIiIjktaioKHr16sWmTZtuWm5GRERERETkWqpxLiIiIiIiIiIiIiJyDSXORURERERERERERESuoVItIiIiIiIiIiIiIiLX0IpzEREREREREREREZFrKHEuIiL3tZEjR1KuXDkyMjLMbSaTiQEDBty1Yw4fPhyTyZSnY0ZFRWEymThy5Ii5rWHDhjRs2DBPj5NbkZGRmEymLI8yZcrk6XFWr16NyWRi9erVeTbmt99+y6RJk/JsvIJi9+7dDB8+3GIOZYqMjCQ4OPiex3SvJSQkMHz48DydT3LrgoODiYyMvGGf232t7uTzd//+/djZ2bF169bb2l9ERERE5Fo2+R2AiIhITk6cOMH48eOJiorCyurB+673k08+ydfjOzo68ttvv2Vpy0tVqlRh/fr1lCtXLs/G/Pbbb9m1axeDBg3KszELgt27dzNixAgaNmyYJUn+9ttv8+KLL+ZPYPdQQkICI0aMAMi3L50kd273tXrmmWdo3rz5bR0zLCyMJ598ksGDB7NmzZrbGkNEREREJJMS5yIict+aPHkyHh4edOjQIb9DuSvyMpl8O6ysrKhVq9ZdPYabm9tdP8aNpKenk5aWhr29fb7FcC+UKlUqv0OQm0hISMDJyemu9S/oMs+3ePHiFC9e/LbHGTBgANWqVWPdunXUqVMnDyMUERERkcLmwVu+JyIiD4SUlBSmT59Ot27dbrraPLMcyHfffcebb75J0aJFcXNzo0mTJuzbty9L/yVLltC4cWPc3d1xcnKibNmyjB079obHMJlMDB8+PEt7diULNmzYQN26dXFwcKBo0aIMHTqU1NTULPteX6rlyJEjmEwm3n//fT744ANCQkJwcXGhdu3abNiwIcv+06ZNIywsDHt7e8qVK8e33357z0p2ZJbL+eyzzyxi+P777y36ZVeqJTIyEhcXF/755x8aN26Ms7MzPj4+DBgwgISEhBset2HDhvzyyy8cPXrUosQM/Hf9xo8fz6hRowgJCcHe3p5Vq1aRlJTESy+9ROXKlXF3d8fLy4vatWvz888/53hus2bNomzZsjg5OVGpUiUWLVpk0e/MmTP07duXwMBA7O3t8fHxoW7duqxYscLcZ/ny5bRt25bixYvj4OBAaGgozz77LGfPns1y3L1799K1a1f8/Pywt7cnKCiIHj16kJycTFRUFE888QQAERER5vOOiooyX9PrX/ekpCSGDh1KSEgIdnZ2FCtWjP79+3Px4kWLfsHBwbRq1YolS5ZQpUoVHB0dKVOmDDNmzLjha5EpOTmZkSNHUrZsWRwcHPD29iYiIoJ169bdciy//fYbDRs2xNvbG0dHR4KCgnj88cdJSEjgyJEj+Pj4ADBixAjzNch8/+Xm9chOZmmQv//+mw4dOuDm5oa7uztPPfUUZ86cydJ/9uzZ1K5dG2dnZ1xcXGjWrBl///23RZ/MOb5z506aNm2Kq6srjRs3vmkMW7dupWPHjnh6epq/DMnttcvtZ1Rm2ahVq1bx/PPPU6RIEby9venQoQMnTpyw2Dc1NZVXX30Vf39/nJyceOSRR9i4ceMNrydw09fqRuebXamWzDn6008/UbFiRRwcHChZsiQfffRRlmNXrVqVsmXLMnXq1JvGKSIiIiJyI1pxLiIi96W//vqLc+fOERERket93njjDerWrcsXX3zBpUuXeO2112jdujV79uzB2toagOnTp9OnTx8aNGjA1KlT8fX1Zf/+/ezatStP4t69ezeNGzcmODiYqKgonJyc+OSTT/j2229zPcb//vc/ypQpY67j/fbbb9OiRQuio6Nxd3cH4PPPP+fZZ5/l8ccf58MPPyQuLo4RI0aQnJyc6+MkJibi7+/PmTNnCAgIoF27dowcORIvL69c7b9gwQJWrVrFyJEjcXZ25pNPPqFr167Y2NjQsWPHG+6bmppKixYtePbZZ3n99ddZt24do0aN4ujRoyxcuDDH/T755BP69u3LoUOH+Omnn7Lt89FHHxEWFsb777+Pm5sbpUuXJjk5mfPnz/Pyyy9TrFgxUlJSWLFiBR06dGDmzJn06NHDYoxffvmFTZs2MXLkSFxcXBg/fjzt27dn3759lCxZEoDu3buzdetWRo8eTVhYGBcvXmTr1q2cO3fOPM6hQ4eoXbs2zzzzDO7u7hw5coQPPviARx55hJ07d2JrawvA9u3beeSRRyhSpAgjR46kdOnSnDx5kgULFpCSkkLLli0ZM2YMb7zxBv/73/+oUqUKkPNKc8MwaNeuHStXrmTo0KHUq1ePHTt2MGzYMNavX8/69estVuFv376dl156iddffx0/Pz+++OILnn76aUJDQ6lfv36Or0daWhqPPfYYf/zxB4MGDaJRo0akpaWxYcMGYmJiqFOnTq5jOXLkCC1btqRevXrMmDEDDw8Pjh8/zpIlS0hJSSEgIIAlS5bQvHlznn76aZ555hkAc4I2N6/HjbRv355OnTrx3HPP8c8///D222+ze/du/vrrL/PrNGbMGN566y169erFW2+9RUpKChMmTKBevXps3LjR4lckKSkptGnTxjzH09LSbhpDhw4d6NKlC8899xzx8fG3/DreimeeeYaWLVvy7bffcuzYMV555RWeeuopi/JNffr04auvvuLll1/m0UcfZdeuXXTo0IHLly/fcOybvVY5ne+NbNu2jUGDBjF8+HD8/f355ptvePHFF0lJSeHll1+26NuwYUN++OEHDMPI8/tViIiIiEghYoiIiNyHxo0bZwBGbGxslm2A0b9/f/PzVatWGYDRokULi35z5swxAGP9+vWGYRjG5cuXDTc3N+ORRx4xMjIycjz2sGHDjOv/igSMYcOGZelbokQJo2fPnubnnTt3NhwdHS3iTktLM8qUKWMARnR0tLm9QYMGRoMGDczPo6OjDcB46KGHjLS0NHP7xo0bDcD47rvvDMMwjPT0dMPf39+oWbOmRSxHjx41bG1tjRIlSuR4bpk++OAD44MPPjCWLVtmLFu2zHjzzTcNJycno0yZMsbly5dvuj+Q43mGhoaa2zJfm1WrVpnbevbsaQDG5MmTLcYcPXq0ARhr16694bFbtmyZ7TlmXr9SpUoZKSkpNxwjLS3NSE1NNZ5++mnj4YcfznJufn5+xqVLl8xtsbGxhpWVlTF27Fhzm4uLizFo0KAbHudaGRkZRmpqqnH06FEDMH7++WfztkaNGhkeHh7G6dOnc9z/hx9+yHItM/Xs2dPimixZssQAjPHjx1v0mz17tgEYn3/+ubmtRIkShoODg3H06FFzW2JiouHl5WU8++yzNzynr776ygCMadOm5dgnt7HMnTvXAIxt27blONaZM2dyfC/e6uuRKfP9PnjwYIv2b775xgCMr7/+2jAMw4iJiTFsbGyMF154waLf5cuXDX9/f6NTp07mtsw5PmPGjFuK4Z133rFov5XXMbefUTNnzjQAo1+/fhb9xo8fbwDGyZMnDcMwjD179tzwulw7ZnZu9FrldL7Xbrv+HEwmU5a58eijjxpubm5GfHy8Rfu0adMMwNizZ88NYxQRERERuRGVahERkfvSiRMnMJlMFClSJNf7tGnTxuJ5xYoVATh69CgA69at49KlS/Tr1++urUJctWoVjRs3xs/Pz9xmbW1N586dcz1Gy5YtzSvkIet57Nu3j9jYWDp16mSxX1BQEHXr1s3VMQYPHszgwYN59NFHefTRRxk1ahRfffUVe/fuZdq0abkaI6fzPHjwIP/+++9N93/yySctnnfr1g24eg3vRJs2bcwrhK/1ww8/ULduXVxcXLCxscHW1pbp06ezZ8+eLH0jIiJwdXU1P/fz88PX19f8GgDUqFGDqKgoRo0axYYNG7Itx3P69Gmee+45AgMDzccsUaIEgPm4CQkJrFmzhk6dOmVZkXu7MlcNX19G6IknnsDZ2ZmVK1datFeuXJmgoCDzcwcHB8LCwizONzu//vorDg4O9O7d+45jqVy5MnZ2dvTt25cvv/ySw4cP3/DY18vN63Ej18/HTp06YWNjY56PS5cuJS0tjR49epCWlmZ+ODg40KBBA4tyRJkef/zxW4rh+v63+jreipt9Xmaed07XJS/cyvUpX748lSpVsmjr1q0bly5dYuvWrRbtvr6+ABw/fvzOgxQRERGRQkuJcxERuS8lJiZia2trkUC+GW9vb4vnmSUMEhMTAcz1iu/kxnM3c+7cOfz9/bO0Z9eWk5udR2bpiWuT1pmya8ut9u3b4+zsnG099ezc6DxvVh7DxsYmy3nmdt+bCQgIyNI2b948OnXqRLFixfj6669Zv349mzZtonfv3iQlJWXpf31scPV1yHwN4Gqt6549e/LFF19Qu3ZtvLy86NGjB7GxsQBkZGTQtGlT5s2bx6uvvsrKlSvZuHGj+fpmjnXhwgXS09PzdF6eO3cOGxubLIl4k8mEv79/lmucm/PNzpkzZyhatOgN70OQ21hKlSrFihUr8PX1pX///pQqVYpSpUoxefLkG8aQ6Wavx81cP58z52hmfKdOnQKgevXq2NraWjxmz56dpW69k5MTbm5uuTp2puvn7q2+jrcit58zOV2XvJDdezUnt/J54+DgAHDT+SsiIiIiciOqcS4iIvelIkWKkJKSQnx8PM7OznkyZmbyKTeroa9nb2+fbf3w7BKQ2SXqcpu8y43MpFVmIi8vj2MYxk1vxnqjY2W23SyxlpaWxrlz5yz65Xbfm8nu1wRff/01ISEhzJ4922L7rdSEv16RIkWYNGkSkyZNIiYmhgULFvD6669z+vRplixZwq5du9i+fTtRUVH07NnTvN/BgwctxvHy8sLa2vq25mVOvL29SUtL48yZMxZJV8MwiI2NpXr16nlyHB8fH9auXUtGRkaO8+ZWYqlXrx716tUjPT2dzZs3M2XKFAYNGoSfnx9dunS5YSw3ez1uJjY2lmLFipmfXz9HM3/9MnfuXPOvBm7kdn7Vcv0+t3LtcvsZlVuZ553TdckLt3KNbuXz5vz58wC39IslEREREZHracW5iIjcl8qUKQNcvbliXqlTpw7u7u5MnToVwzBuad/g4GB27Nhh0fbbb79x5coVi7aIiAhWrlxpkdROT09n9uzZtx/4dcLDw/H392fOnDkW7TExMaxbt+62x507dy4JCQnUqlUrV/1zOs9SpUrlavX0N998Y/E88waqDRs2vOF+uVkJfT2TyYSdnZ1Foi42Npaff/75lsbJSVBQEAMGDODRRx81l43IPNb1N2/87LPPLJ47OjrSoEEDfvjhhyyrlq91/YrgG2ncuDFw9QuDa/3444/Ex8ebt9+pxx57jKSkJKKiovI0Fmtra2rWrMn//vc/APM1ze01yO71uJnr5+OcOXNIS0szz8dmzZphY2PDoUOHqFatWraPvHYr1y63n1G5lXneOV2Xm7mV+Zob//zzD9u3b7do+/bbb3F1dTXfLDfT4cOHsbKyIjw8PE+OLSIiIiKFk1aci4jIfSkzabNhwwZz7d075eLiwsSJE3nmmWdo0qQJffr0wc/Pj4MHD7J9+3Y+/vjjHPft3r07b7/9Nu+88w4NGjRg9+7dfPzxx7i7u1v0e+utt1iwYAGNGjXinXfewcnJif/973/Ex8fnyTkAWFlZMWLECJ599lk6duxI7969uXjxIiNGjCAgIOCmK8aPHj1Kt27d6NKlC6GhoZhMJtasWcOkSZMoX748zzzzTK7iKFKkCI0aNeLtt9/G2dmZTz75hL179/L999/fdF87OzsmTpzIlStXqF69OuvWrWPUqFE89thjPPLIIzfc96GHHmLevHl8+umnVK1aFSsrq5smLVu1asW8efPo168fHTt25NixY7z77rsEBARw4MCBXJ3vteLi4oiIiKBbt26UKVMGV1dXNm3axJIlS+jQoQNw9cufUqVK8frrr2MYBl5eXixcuJDly5dnGe+DDz7gkUceoWbNmrz++uuEhoZy6tQpFixYwGeffYarqysVKlQA4PPPP8fV1RUHBwdCQkKyXaH/6KOP0qxZM1577TUuXbpE3bp12bFjB8OGDePhhx+me/fut3zO2enatSszZ87kueeeY9++fURERJCRkcFff/1F2bJl6dKlS65jmTp1Kr/99hstW7YkKCiIpKQkZsyYAUCTJk0AcHV1pUSJEvz88880btwYLy8vihQpgqen501fj5uZN28eNjY2PProo/zzzz+8/fbbVKpUyXwvgeDgYEaOHMmbb77J4cOHad68OZ6enpw6dYqNGzfi7OzMiBEj8uS6ZrqV1zG3n1G5VbZsWZ566ikmTZqEra0tTZo0YdeuXbz//vu5KkGT02sVHBx8W/EULVqUNm3aMHz4cAICAvj6669Zvnw548aNw8nJyaLvhg0bqFy5Mp6enrd1LBERERER4Lpb1ouIiNxH6tWrZ7Ro0SJLO2D079/f/HzVqlUGYPzwww8W/aKjow3AmDlzpkX74sWLjQYNGhjOzs6Gk5OTUa5cOWPcuHHm7cOGDTOu/ysyOTnZePXVV43AwEDD0dHRaNCggbFt2zajRIkSRs+ePS36/vnnn0atWrUMe3t7w9/f33jllVeMzz//3ACM6Ohoc78GDRoYDRo0yBLvhAkTsj3nYcOGWbR9/vnnRmhoqGFnZ2eEhYUZM2bMMNq2bWs8/PDDWfa/1vnz54327dsbwcHBhqOjo2FnZ2eULl3aePXVV42LFy/ecN9r4+nfv7/xySefGKVKlTJsbW2NMmXKGN98841Fv8zXZtWqVea2nj17Gs7OzsaOHTuMhg0bGo6OjoaXl5fx/PPPG1euXLnpsc+fP2907NjR8PDwMEwmk/m1utH1MwzDeO+994zg4GDD3t7eKFu2rDFt2rRsX+vr51ema1/rpKQk47nnnjMqVqxouLm5GY6OjkZ4eLgxbNgwIz4+3rzP7t27jUcffdRwdXU1PD09jSeeeMKIiYnJ9vXcvXu38cQTTxje3t6GnZ2dERQUZERGRhpJSUnmPpMmTTJCQkIMa2tri7nds2dPo0SJEhbjJSYmGq+99ppRokQJw9bW1ggICDCef/5548KFC1nOq2XLllnO9/r5mZPExETjnXfeMUqXLm3Y2dkZ3t7eRqNGjYx169bdUizr16832rdvb5QoUcKwt7c3vL29jQYNGhgLFiywON6KFSuMhx9+2LC3tzcAo2fPnrl+PbKTOQe2bNlitG7d2nBxcTFcXV2Nrl27GqdOncrSf/78+UZERITh5uZm2NvbGyVKlDA6duxorFixwtwnc47nVmYMZ86cybItt69jbj+jZs6caQDGpk2bLPbP7r2anJxsvPTSS4avr6/h4OBg1KpVy1i/fn22n3vZye61utn5ZveezJyjc+fONcqXL2/Y2dkZwcHBxgcffJBl/8uXLxtOTk7GxIkTbxqfiIiIiMiNmAzjFn+rLiIico/8+OOPdO7cmaNHj1rU2JXsXbx4kbCwMNq1a8fnn39+V49lMpno37//DVfp5yQyMpK5c+fedgkJkbw0fPhwRowYwZkzZ1QT+z4VHBxMhQoVWLRo0U37Tp8+nRdffJFjx45pxbmIiIiI3BHVOBcRkftWhw4dqF69OmPHjs3vUO47sbGxvPDCC8ybN481a9bw1VdfERERweXLl3nxxRfzOzwRkXsuLS2NcePGMXToUCXNRUREROSOqca5iIjct0wmE9OmTWPBggVkZGTctHZ3YWJvb8+RI0fo168f58+fx8nJiVq1ajF16lTKly+f3+GJiNxzx44d46mnnuKll17K71BERERE5AGgUi0iIiIiIiIiIiIiItfQ0j0RERERERERERERkWsocS4iIiIiIiIiIiIicg0lzkVERERERERERERErqHEuYiIiIiIiIiIiIjINWzyO4AHSUZGBidOnMDV1RWTyZTf4YiIiIiIiIiI3DHDMLh8+TJFixbFykprMEWkcFDiPA+dOHGCwMDA/A5DRERERERERCTPHTt2jOLFi+d3GCIi94QS53nI1dUVuPoXiZubWz5Hc/elpqaybNkymjZtiq2tbX6HIw8gzTEpDDTP5V7QPJOCSnNX7jbNMSks7nSuX7p0icDAQHPeQ0SkMFDiPA9llmdxc3MrNIlzJycn3Nzc9D+ZcldojklhoHku94LmmRRUmrtyt2mOSWGRV3NdZWlFpDBRYSoRERERERERERERkWsocS4iIiIiIiIiIiIicg0lzkVERERERERERERErqEa5yIiIiIiIiIiItkwDIO0tDTS09PzOxQRuUPW1tbY2Njk+n4NSpyLiIiIiIiIiIhcJyUlhZMnT5KQkJDfoYhIHnFyciIgIAA7O7ub9lXiXERERERERERE5BoZGRlER0djbW1N0aJFsbOzy/UqVRG5/xiGQUpKCmfOnCE6OprSpUtjZXXjKuZKnIuIiIiIiIiIiFwjJSWFjIwMAgMDcXJyyu9wRCQPODo6Ymtry9GjR0lJScHBweGG/XVzUBERERERERERkWzcbEWqiBQst/Ke1rtfREREREREREREROQaSpyLiIiIiIiIiIjIfSsqKgoPD497cqzVq1djMpm4ePHifRGP5B8lzkVERERERERERB4QkZGRmEwm88Pb25vmzZuzY8eO/A7trjKZTMyfPz9Le2RkJO3atbvn8UjBp8S5iIiIiIiIiIjIA6R58+acPHmSkydPsnLlSmxsbGjVqlV+hyVSoChxLiIiIiIiIiIi8gCxt7fH398ff39/KleuzGuvvcaxY8c4c+YMkH05km3btmEymThy5Ajx8fG4ubkxd+5ci3EXLlyIs7Mzly9fzva4S5Ys4ZFHHsHDwwNvb29atWrFoUOHzNuPHDmCyWRi3rx5RERE4OTkRKVKlVi/fr3FOFFRUQQFBeHk5ET79u05d+5cHl0ZSE5OZuDAgfj6+uLg4MAjjzzCpk2bbrjP3YxH7l9KnIuIiIiIiIiIiDygrly5wjfffENoaCje3t652sfZ2ZkuXbowc+ZMi/aZM2fSsWNHXF1ds90vPj6eIUOGsGnTJlauXImVlRXt27cnIyPDot+bb77Jyy+/zLZt2wgLC6Nr166kpaUB8Ndff9G7d2/69evHtm3biIiIYNSoUbdx5tl79dVX+fHHH/nyyy/ZunUroaGhNGvWjPPnz2fb/27HI/cvm/wOQEREREREREREpCCoVq0asbGx9/SY/v7+bN68+Zb2WbRoES4uLsDVZHZAQACLFi3Cyir3a2ifeeYZ6tSpw4kTJyhatChnz55l0aJFLF++PMd9Hn/8cYvn06dPx9fXl927d1OhQgVz+8svv0zLli0BGDFiBOXLl+fgwYOUKVOGyZMn06xZM15//XUAwsLCWLduHUuWLLlpzF27dsXa2tqiLTk52Xys+Ph4Pv30U6KionjssccAmDZtGsuXL2f69Om88sorWca8k3ikYFPiXEREREREREREJBdiY2M5fvx4fodxUxEREXz66acAnD9/nk8++YTHHnuMjRs3UqJEiVyNUaNGDcqXL89XX33F66+/zqxZswgKCqJ+/fo57nPo0CHefvttNmzYwNmzZ80rzWNiYiwS5xUrVjT/OSAgAIDTp09TpkwZ9uzZQ/v27S3GrV27dq4S1R9++CFNmjSxaHvttddIT083x5eamkrdunXN221tbalRowZ79uzJdsw7iUcKNiXORUREREREREREcsHf379AHNPZ2ZnQ0FDz86pVq+Lu7s60adMYNWqUeeW5YRjmPqmpqVnGeeaZZ/j44495/fXXmTlzJr169cJkMuV43NatWxMYGMi0adMoWrQoGRkZVKhQgZSUFIt+tra25j9njpeZZL82plvl7+9vcd4Arq6u5lrumWNffw6GYeR4XncSjxRsSpyLiIiIiIiIiIjkwq2WTLlfmEwmrKysSExMBMDHxweAkydP4unpCVy9Oej1nnrqKV599VU++ugj/vnnH3r27JnjMc6dO8eePXv47LPPqFevHgBr16695VjLlSvHhg0bLNquf367QkNDsbOzY+3atXTr1g24+oXB5s2bGTRo0D2PR+5vSpyLiIiIiIiIiIg8QJKTk8212C9cuMDHH3/MlStXaN26NXA1gRwYGMjw4cMZNWoUBw4cYOLEiVnG8fT0pEOHDrzyyis0bdqU4sWL53hMT09PvL29+fzzzwkICCAmJsZcF/xWDBw4kDp16jB+/HjatWvHsmXL8qwsirOzM88//zyvvPIKXl5eBAUFMX78eBISEnj66afveTxyf8v9HQFERERERERERETkvrdkyRICAgIICAigZs2abNq0iR9++IGGDRsCV0ulfPfdd+zdu5dKlSoxbtw4Ro0ale1YTz/9NCkpKfTu3fuGx7SysuL7779ny5YtVKhQgcGDBzNhwoRbjr1WrVp88cUXTJkyhcqVK7Ns2TLeeuutWx4nJ++99x6PP/443bt3p0qVKhw8eJClS5eaV97f63jk/qUV5yIiIiIiIiIiIg+IqKgooqKibtqvbt267Nixw6Itu3reJ0+exNvbm7Zt2950zCZNmrB79+4cxwwODs5yDA8PjyxtvXv3zpKof+mll2547JxqkV9/LRwcHPjoo4/46KOPsu3fsGHDPIlHCj4lzkVERERERERERMRCQkIC0dHRjB07lmeffRY7O7v8DknknlKpFhEREREREREREbEwfvx4KleujJ+fH0OHDs3vcETuOSXORURERERERERExMLw4cNJTU1l5cqVuLi45Hc4IvecEuciIiIiIiIiIiIiItdQ4lxERERERERERERE5BpKnIuIiIiIiIiIiIiIXMMmvwMQERERERERuRXpqRlYWZswWZkAiD0cx4mDF0m8nEri5RQy0g2CynlR8mEf7Bz0z14RERG5dfo/CBEREREREbnvJF5JIe5MojkZfvWRSsKlFFKT0qjXOQy3Io4AXLmQzMkDFy32P7T1NEd3naPkwz6UrOSDta1+cC0iIiK5p8S5iIiIiIiI3DOGYZCSlE7ipf+S4YlXUki4lEK5ukVx9rAH4Pi+i+zbcDLHcZLiU82J8yLFXbCqUxRHV1scXe1Iik9l/8ZTXD6XyJEdZwmpVOSenJuIiIg8OJQ4FxERERERkTxjGAbJCWnmVeLexV2xd7z6T8+j/5xjz9oTpKdlZLtvUHlvc+LcxdMedx8nHN2uJsOvPmxx+v8/29pbm/fz8HPCw8/JYiy/EDdOHryIYYCNrbU5tpOH4vAv6Y7V/5d5ERGRWxMcHMygQYMYNGjQbe0fFRXFoEGDuHjxYp7GJZLXlDgXERERERGRPBF7OI5tK2JIT/0vMV6tZQh+wW4A2NhamZPm9s62FslwBxdbXL0czPv5l3THv6T7bcdiMpkoWtrTou3koTj+XnoUJw97wmv4UaSE822PLyJyv4qMjOTixYvMnz//roy/adMmnJ1z9/mZXZK9c+fOtGjRIk9iadiwIWvWrAGufu77+PhQv3593n//fUqUKJEnx5DCS4lzERERERERuWMZ6Rns+fPE1aS5yYSDy9WEuJX1fyu7fUu40fCpMji42GJtfe9rjhvpBnaONiRcTObvZTE4e9qRnGJFanI6tra29zweEZGCyMfH5472d3R0xNHRMY+igT59+jBy5EgMw+Do0aMMGjSIp556ij/++CPPjiGFk+6OIiIiIiIiInfs+IGLJFxKwdbBhmbPlKdxj7LUbl8Kn0BXcx9be2uc3e3zJWkOUCzck4inyhBW0x8bO2sun0/m8iF7Vkbt5ffv91v0TYpPxcgw8iVOEZG7Zc2aNdSoUQN7e3sCAgJ4/fXXSUtLM2+/fPkyTz75JM7OzgQEBPDhhx/SsGFDixXjwcHBTJo0yfx8+PDhBAUFYW9vT9GiRRk4cCBwdTX40aNHGTx4MCaTCZPp6hepUVFReHh4WMS1YMECqlWrhoODA0WKFKFDhw65PicnJyf8/f0JCAigVq1a9O/fn61bt5q3p6en8/TTTxMSEoKjoyPh4eFMnjzZYozVq1dTo0YNnJ2d8fDwoG7duhw9etS8feHChVStWhUHBwdKlizJiBEjLK6bPJi04lxERERERETuSEaGwcHNpwEoVcUHGzvrm+yRf2zsrCldzY8SFbw5sDmWs6tjAbC1t0zmr//pEEnxqbh6OeDq7YBbEUe8izqbb0gqIoVXUmp6jtusTCbsbKzyrK+Dbd59nh4/fpwWLVoQGRnJV199xd69e+nTpw8ODg4MHz4cgCFDhvDnn3+yYMEC/Pz8eOedd9i6dSuVK1fOdsy5c+fy4Ycf8v3331O+fHliY2PZvn07APPmzaNSpUr07duXPn365BjXL7/8QocOHXjzzTeZNWsWKSkp/PLLL7d1jufPn+eHH36gZs2a5raMjAyKFy/OnDlzKFKkCOvWraNv374EBATQqVMn0tLSaNeuHX369OG7774jJSWFjRs3mhP9S5cu5amnnuKjjz6iXr16HDp0iL59+wIwbNiw24pTCgYlzkVEREREROTOGAYlK/twbPd5SlTwzu9ocsXOwYawmn4cPLeFxo3LYKT/V1ImPS2D5IRUMtIyiDudQNzphKsbTCaqNCtBQKnbr70uIgVf/2+25rjtoeLuDGoSZn4+ePY2UnK4IXKYvyuvNS9jfv7ajzu4kmS5inl6ZPU7jPY/n3zyCYGBgXz88ceYTCbKlCnDiRMneO2113jnnXeIj4/nyy+/5Ntvv6Vx48YAzJw5k6JFi+Y4ZkxMDP7+/jRp0gRbW1uCgoKoUaMGAF5eXlhbW+Pq6oq/v3+OY4wePZouXbowYsQIc1ulSpVu6by++OILDMMgISGBsLAwli5dat5ua2trMXZISAjr1q1jzpw5dOrUiUuXLhEXF0erVq0oVaoUAGXLlrWI7/XXX6dnz54AlCxZknfffZdXX31VifMHnEq1iIiIiIiIyB2xsraiRAVv6j4Rik0ero68VzJLyGSytrGi2TMVaNCtDA83K0FoVV88A5zBMNi15l9SEvXzfBEpePbs2UPt2rXNK6kB6taty5UrV/j33385fPgwqamp5sQ3gLu7O+Hh4TmO+cQTT5CYmEjJkiXp06cPP/300y2XMNm2bZs5UX87nnzySbZt28b27dtZu3YtoaGhNG3alMuXL5v7TJ06lWrVquHj44OLiwvTpk0jJiYGuJrgj4yMpFmzZrRu3ZrJkydz8uRJ875btmxh5MiRuLi4mB99+vTh5MmTJCQk3Hbccv/TinMRERERERHJE9cmYwo6k5UJF097XDztIdSD9PQM1s45QEa6QVJ8KnaO+ue0SGH1vyer5LjN6rrPwQ87V85133GPV7yjuG7GMIwsn9OGcfVeDiaTyeLP2fXJTmBgIPv27WP58uWsWLGCfv36MWHCBNasWZPrmy7f6Y1C3d3dCQ0NBSA0NJTp06cTEBDA7NmzeeaZZ5gzZw6DBw9m4sSJ1K5dG1dXVyZMmMBff/1lHmPmzJkMHDiQJUuWMHv2bN566y2WL19OrVq1yMjIYMSIEdnWXXdwcLij2OX+pr/pRURERERE5LZkZBhs/iWagNIeFAvzxMrqwUmcX8/a2opqLYJxcLLF2lY/3hYpzG6l7vjd6ns7ypUrx48//miRQF+3bh2urq4UK1YMDw8PbG1t2bhxI4GBgQBcunSJAwcO0KBBgxzHdXR0pE2bNrRp04b+/ftTpkwZdu7cSZUqVbCzsyM9Pec67wAVK1Zk5cqV9OrVK0/O09r66nVMTEwE4I8//qBOnTr069fP3OfQoUNZ9nv44Yd5+OGHGTp0KLVr1+bbb7+lVq1aVKlShX379pmT81J45Ovf9r///jutW7emaNGimEwm5s+fb7F93rx5NGvWjCJFimAymdi2bZvF9vPnz/PCCy8QHh6Ok5MTQUFBDBw4kLi4OIt+Fy5coHv37ri7u+Pu7k737t25ePGiRZ+YmBhat26Ns7MzRYoUYeDAgaSkpNyFsxYREREREXkwnNh/gTMxl9nz50ky0rOv4fsgcXa3V9JcRAqEuLg4tm3bZvHo27cvx44d44UXXmDv3r38/PPPDBs2jCFDhmBlZYWrqys9e/bklVdeYdWqVfzzzz/07t0bKyurHH9RFBUVxfTp09m1axeHDx9m1qxZODo6UqJECQCCg4P5/fffOX78OGfPns12jGHDhvHdd98xbNgw9uzZw86dOxk/fnyuzzUhIYHY2FjzjUn79euHg4MDTZs2Ba6uQt+8eTNLly5l//79vP3222zatMm8f3R0NEOHDmX9+vUcPXqUZcuWsX//fnOd83feeYevvvqK4cOH888//7Bnzx7zqnR5sOXr3/jx8fFUqlSJjz/+OMftdevW5b333st2+4kTJzhx4gTvv/8+O3fuJCoqiiVLlvD0009b9OvWrRvbtm1jyZIlLFmyhG3bttG9e3fz9vT0dFq2bEl8fDxr167l+++/58cff+Sll17Ku5MVERERERF5gGRkGBzYchqAkpV9CmRt89tlZBgc3naGf/44nt+hiIhka/Xq1eYV1JmPYcOGsXjxYjZu3EilSpV47rnnePrppy0SwB988AG1a9emVatWNGnShLp161K2bNkcS5J4eHgwbdo06tata145vnDhQry9r94oeuTIkRw5coRSpUrh4+OT7RgNGzbkhx9+YMGCBVSuXJlGjRpZlFG5mWnTphEQEEBAQAARERGcOXOGxYsXm2uzP/fcc3To0IHOnTtTs2ZNzp07Z7H63MnJib179/L4448TFhZG3759GTBgAM8++ywAzZo1Y9GiRSxfvpzq1atTq1YtPvjgA/OXA/LgMhk3KlR0D5lMJn766SfatWuXZduRI0cICQnh77//pnLlyjcc54cffuCpp54iPj4eGxsb9uzZQ7ly5diwYQM1a9YEYMOGDdSuXZu9e/cSHh7Or7/+SqtWrTh27Jj5TsHff/89kZGRnD59Gjc3t1ydw6VLl3B3dycuLi7X+xRkqampLF68mBYtWuS6bpXIrdAck8JA81zuBc0zKag0d+9v/+67wPYVMdg62NCoR5kCmTi/3Tl28VQCf849AECNNiXxCXS9WyGK5Ik7/TwtbPkOgKSkJKKjowkJCSnUdazj4+MpVqwYEydOzLJQVaQgupX39gNX4zzzQ9zG5uqprV+/Hnd3d3PSHKBWrVq4u7uzbt06wsPDWb9+PRUqVDAnzeHqt0nJycls2bKFiIiIbI+VnJxMcnKy+fmlS5eAq38hpaam3o3Tu69knmNhOFfJH5pjUhhonsu9oHkmBZXm7v0rI8Ng38aTZBgGJR7yxCCD1NSCV6rldueYs5ctxct5EvPPebavjKHuE6WwsSt4XxxI4XGnn6f6HC48/v77b/bu3UuNGjWIi4tj5MiRALRt2zafIxO59x6oxPm5c+d49913zT+lAIiNjcXX1zdLX19fX2JjY819/Pz8LLZ7enpiZ2dn7pOdsWPHMmLEiCzty5Ytw8nJ6XZPo8BZvnx5focgDzjNMSkMNM/lXtA8k4JKc/f+k3TOmivRdphsDNL/Pcrek/kd0Z25nTlmpMOFi/ZknLLi1IyjuAQpsSj3v9v9PE1ISMjjSOR+9v7777Nv3z7s7OyoWrUqf/zxB0WKFLnncfzxxx889thjOW6/cuXKPYxGCqMHJnF+6dIlWrZsSbly5Rg2bJjFtuxuYHDtHYRz2+d6Q4cOZciQIRYxBAYG0rRp00Lx06XU1FSWL1/Oo48+qp/Oyl2hOSaFgea53AuaZ1JQae7en4wMg7VzDuLkl0JYDV9KPpx9zdqC4E7n2NmHr7D5l6MA1KgcjFdR57wOUSRP3Olcz/yFvTz4Hn74YbZs2ZLfYQBQrVo1tm3blt9hSCH2QCTOL1++TPPmzXFxceGnn36y+EvA39+fU6dOZdnnzJkz5lXm/v7+WW46cOHCBVJTU7OsRL+Wvb099vb2WdptbW0L1f/YF7bzlXtPc0wKA81zuRc0z6Sg0ty9/1RqFET09jOUquxXIGubX+9251hAiCclyl/h2O7z7P4jlvqdw7C2tboLEYrkjdud6/oMlvzg6OhIaGhofochhViB/xv90qVLNG3aFDs7OxYsWJClqHvt2rWJi4tj48aN5ra//vqLuLg46tSpY+6za9cuTp787/eFy5Ytw97enqpVq96bExERERERESkgvIu5UK1FiOp6A2XrFMXB2ZbEyymcPxmf3+HcVwzDwDCMPB3z3IkrbP71CNtXHiPmn3NcPp+U58cQERGBfF5xfuXKFQ4ePGh+Hh0dzbZt2/Dy8iIoKIjz588TExPDiRMnANi3bx9wdYW4v78/ly9fpmnTpiQkJPD1119z6dIl88+HfHx8sLa2pmzZsjRv3pw+ffrw2WefAdC3b19atWpFeHg4AE2bNqVcuXJ0796dCRMmcP78eV5++WX69OlTKEquiIiIiIiI5MbNylkWRrb21lR+NAhbe2vcijjmdzj3jZSkNP788SDJV1Jx9rTHxcMBFy97XDwdcPG0x9ndDivr7NfyGYZBwqUULsYmcCE2noBQD7yLuQCQnprBqcNxAPy79zwANvbWePo54RngTEApD1w8s/4yXERE5Fbla+J88+bNREREmJ9n1gvv2bMnUVFRLFiwgF69epm3d+nSBYBhw4YxfPhwtmzZYi6xcv1PN6KjowkODgbgm2++YeDAgTRt2hSANm3a8PHHH5v7Wltb88svv9CvXz/q1q2Lo6Mj3bp14/3338/7kxYRERERESmAjAyDP388SJHiLpSq4outvVabZ8pM6sp/UpPTsbWzJiEtmUtnErl0JtFiu3cxF2q1K2V+fuLABRIupXLxVDwXYhNISUwzb7OxszZfY09/Z8rWKUpqchrnYxOIO5VAWnI6Z2IucybmMq5eDubE+eXzSVw+l4iHnzOOrrb60kdERG5JvibOGzZseMOfVEVGRhIZGXnb+2fy8vLi66+/vmGfoKAgFi1adNOxRERERERECqMTBy8SdzqBhLhkSlX1ze9w7ltxZxK5eDqBEuW98zuUfOXsbk+dDqU4fzKBtNR0rpxP5sqFJK5cSObKhWScPf5bFZ6Wms7fy2Is9jdZmXD3ccTT35kiga7mdlt7a4sb0makZ3DpXBIX/n91umfAfzdoPXnwIgc2Xb3nmYOzLZ4Bznj4OeEV4IxbEYccV7yLiIjAA3JzUBEREREREbl7jAyDA5tPAxBS2Qdb1TbP1pULSfw59wCGAR6+Trj7FO7SLVbWVhQp/v+r8UP+azcMg4z0/xbBpSan413cBVt7azz9nPHwv3rtrG1unti2srbCw9cJD18nQioWsdhm72SLu68Tl84mkhSfysmDFzl58OLV/WysqN8lDGf3qwl8I8PAZKUV6SIi8h8lzkVEREREROSGTh66SPyFJGztrQm+Ljkp/3HxdMC/pDsnD15kx2/HqNsxtNCtav533wWuXEgitKovNrbZf8FiMpmwtvkvSe3oYkettqWy7XsnSlTwpkQFb9JTM7h4OsG8Kv1CbAJGhoGTq5257/bfjnHxVAKe/s54Bjjh6e+Mi6e9yruI3EBwcDCDBg1i0KBBedpX5H6hxLmIiIiIiIjkyMgw2L9Jq81zq3z9Ypz99wqXziZyfN9FAst55XdI90xKUhq7154gNSkNB2dbgh+6P75ksba1wruYi7lOumEYJMWnWqwwvxB7tQxR/MVk801Hbe2t8fB3xivAmVJVfJRElwIjMjKSL7/8EgAbGxu8vLyoWLEiXbt2JTIyEiurvPlCb9OmTTg7O9+84y32zY2bvR8z7594Lw0fPpwRI0aYn7u5uVGxYkVGjRpFgwYN7mkskjcK11ffIiIiIiIicksO/X3mv9Xm90ki9H5m72hDaJWrNeAP/n0aI+Pm9+V6UOzfeIrUpDRcPB0Iuo+/MDCZTDi62Fm01e0YSrWWIYRW9cWrmAtWNlakJqdz5uglThy4aJGkO7ztDCcOXCA1Jf1ehy6Sa82bN+fkyZMcOXKEX3/9lYiICF588UVatWpFWlrazQfIBR8fH5ycnPK8b26cPHnS/Jg0aRJubm4WbZMnT7bon5qammfHvpHy5cubY1i/fj2lS5emVatWxMXF3ZPjS95S4lxERERERESylZ6awbH/X30bWtUPW3utNs+NoPJe2Npbk3AxmZOHC0eyJO5MIkd3nQOurrovaCVq7Bxs8At2I7xWALXblaLZM+Wp27E05eoVI7jifzd6TU/PYN+GWP5eFsOab/Zx4sAFDKPwfDkiBYe9vT3+/v4UK1aMKlWq8MYbb/Dzzz/z66+/mldix8XF0bdvX3x9fXFzc6NRo0Zs377dYpwFCxZQrVo1HBwcKFKkCB06dDBvCw4OZtKkSebnw4cPJygoCHt7e4oWLcrAgQNz7BsTE0Pbtm1xcXHBzc2NTp06cerUKYuxKleuzKxZswgODsbd3Z0uXbpw+fJlAPz9/c0Pd3d3TCaT+XlSUhIeHh7MmTOHhg0b4uDgwNdffw3AzJkzKVu2LA4ODpQpU4ZPPvnE4nyPHz9O586d8fT0xNvbm7Zt23LkyJFcX3cbGxtzHOXKlWPEiBFcuXKF/fv3m/t88MEHPPTQQzg7OxMYGEi/fv24cuWKefvRo0dp3bo1np6eODs7U758eRYvXmzevnv3blq0aIGLiwt+fn50796ds2fP5jpGyb2C9TeZiIiIiIiI3DPWtlbUaBVC2bpFCams1ea5ZWP3Xy34Q1tOP/CJVcMw2PX7cTAMAkp7/HdD0ALMytoKD7+rNxwNKndN4jw1g6DyXji62ZGckMrfy2LYuDCaKxeS8zFauedSk3J+pKXkbd881KhRIypVqsS8efMwDIOWLVsSGxvL4sWL2bJlC1WqVKFx48acP3/1C9NffvmFDh060LJlS/7++29WrlxJtWrVsh177ty5fPjhh3z22WccOHCA+fPn89BDD2Xb1zAM2rVrx/nz51mzZg3Lly/n0KFDdO7c2aLfoUOHmD9/PosWLWLRokWsWbOG9957L9fn+9prrzFw4ED27NlDs2bNmDZtGm+++SajR49mz549jBkzhrfffttc1iYhIYGIiAhcXFz4/fffWbt2LS4uLjRv3pyUlJSbHC2r5ORkoqKi8PDwIDw83NxuZWXFRx99xK5du/jyyy/57bffePXVV83b+/fvT3JyMr///js7d+5k3LhxuLhc/Vw9efIkDRo0oHLlymzevJklS5Zw6tQpOnXqdMvxyc2pxrmIiIiIiIiYpSSlcf5kPP4h7gA4u9tTsrJPPkdV8ARXLMKJg3EUL+OJYcCDXB77370XuBgbj7WtFeXqFM3vcO4qOwcbytcrRpnaARz++wwHt5zm7LHL/P79PkpV8SW0qi/WNlqj+MD7oWfO24o+DA1f/+/5vD6QnkPS1bcsNBn+3/MFAyD5smWfbrNvO8zslClThh07drBq1Sp27tzJ6dOnsbe3B+D9999n/vz5zJ07l759+zJ69Gi6dOliUbe7UqVK2Y4bExODv78/TZo0wdbWlqCgIGrUqJFt3xUrVrBjxw6io6MJDAwEYNasWZQvX55NmzZRvXp1ADIyMoiKisLV1RWA7t27s3LlSkaPHp2rcx00aJDFCvl3332XiRMnmttCQkLYvXs3n332GT179uT777/HysqKL774wlyeaebMmXh4eLB69WqaNm1602Pu3LnTnOROSEjA1dWV2bNn4+bmZhFXppCQEN59912ef/558+r3mJgYHn/8cfMXDyVLljT3//TTT6lSpQpjxowxt82YMYPAwED2799PWFhYrq6N5I4+zUVERERERASA+Lhk1v14kC2/HuX00Uv5HU6BZudgQ4OuYYRU8sHK6sHNmhsZBgc2XS2vEFbdHwcX23yO6N6wtrGidHU/6ncNwyfIFSPDIHrbGVIS86Z2tMjdYhgGJpOJLVu2cOXKFby9vXFxcTE/oqOjOXToEADbtm2jcePGuRr3iSeeIDExkZIlS9KnTx9++umnHGup79mzh8DAQHPSHKBcuXJ4eHiwZ88ec1twcLA5aQ4QEBDA6dOnc32u166OP3PmDMeOHePpp5+2ON9Ro0aZz3fLli0cPHgQV1dX83YvLy+SkpLMfW4mPDycbdu2sW3bNrZs2cLzzz/PE088webNm819Vq1axaOPPkqxYsVwdXWlR48enDt3jvj4eAAGDhzIqFGjqFu3LsOGDWPHjh3mfbds2cKqVasszqFMmTIAuY5Rck8rzkVERERERISLpxLY9Es0KYlpOLja4ehqd/Od5IZMBWiZ+ZULyRzbc46wGv63tGLaZGWidvtSRG8/a1ELvLBwdreneqsQYg9fIjU5zeJ9k5KUhp2D0i4PpCe+zHmb6br3T4dpue/b5uPbjymX9uzZQ0hICBkZGQQEBLB69eosfTw8PABwdHTM9biBgYHs27eP5cuXs2LFCvr168eECRNYs2YNtraWX6hlJu+vd3379fuZTCYyMjJyHZOzs7P5z5n7TZs2jZo1a1r0s7a2NvepWrUq33zzTZaxfHxy98srOzs7QkNDzc8ffvhh5s+fz6RJk/j66685evQoLVq04LnnnuPdd9/Fy8uLtWvX8vTTT5tvYPrMM8/QrFkzfvnlF5YtW8bYsWOZOHEiL7zwAhkZGbRu3Zpx48ZlOXZAQECuYpTc0ye4iIiIiIhIIRcbHcffy2LISMvArYgj1VuF4OBcOFYO320ZGQYnD14k7nQi5R65P8qYXD6fxIFNp/ANdqN4uCeGYbB9ZQwXTyVw+uhlKjcJwt0n9wkzR1e7++bc8oPJZCKglLtF29l/r7B5cTRhNfwJrljkgf7VQaFk65D/fW/Db7/9xs6dOxk8eDDFixcnNjYWGxsbgoODs+1fsWJFVq5cSa9evXI1vqOjI23atKFNmzb079+fMmXKsHPnTqpUqWLRr1y5csTExHDs2DHzqvPdu3cTFxdH2bJl7+gcc+Ln50exYsU4fPgwTz75ZLZ9qlSpwuzZs803S80r1tbWJCYmArB582bS0tKYOHEiVlZXvziZM2dOln0CAwN57rnneO655xg6dCjTpk3jhRdeoEqVKvz4448EBwdjY6O07t2mUi0iIiIiIiKF2JGdZ9ny61Ey0jLwCXKldvtSSprnocTLKWxbcYzo7We4dDYxX2OJj0tm24oYfv9+PycPXuTAplPmFZ6h1fywc7Lhyvkk/px7gINbTmNk5HxTU8MwuHgq4R5GX7Ac33eB9NQM9vx5grVzDpCWmp7fIUkhk5ycTGxsLMePH2fr1q2MGTOGtm3b0qpVK3r06EGTJk2oXbs27dq1Y+nSpRw5coR169bx1ltvmcuKDBs2jO+++45hw4axZ88edu7cyfjx47M9XlRUFNOnT2fXrl0cPnyYWbNm4ejoSIkSJbL0bdKkCRUrVuTJJ59k69atbNy4kR49etCgQYMcbz6aF4YPH87YsWOZPHky+/fvZ+fOncycOZMPPvgAgCeffJIiRYrQtm1b/vjjD6Kjo1mzZg0vvvgi//77b66OkZaWRmxsLLGxsRw4cIBRo0axe/du2rZtC0CpUqVIS0tjypQp5us0depUizEGDRrE0qVLiY6OZuvWrfz222/mLxT69+/P+fPn6dq1Kxs3buTw4cMsW7aM3r17k56uz5m8psS5iIiIiIhIIXX23yv88/txMAyKl/WiWotgbOys8zusB4qzu715NfLBrbmvzZuXEi6lsP23Y6z+Zh/H910Aw8C/lDtVHythLovgF+xG/S7h+JV0x8gw2LfhJOt/OkR8XHK2Y/677wJ/zj3AztW5SyYVNhUbFadiRCC2Dja4FXHAxlbvK7m3lixZQkBAAMHBwTRv3pxVq1bx0Ucf8fPPP2NtbY3JZGLx4sXUr1+f3r17ExYWRpcuXThy5Ah+fn4ANGzYkB9++IEFCxZQuXJlGjVqxF9//ZXt8Tw8PJg2bRp169Y1r1RfuHAh3t5ZSziZTCbmz5+Pp6cn9evXp0mTJpQsWZLZs/P2RqjXe+aZZ/jiiy+IiorioYceokGDBkRFRRESEgKAk5MTv//+O0FBQXTo0IGyZcvSu3dvEhMTc70C/Z9//iEgIICAgAAqV67MnDlz+PTTT+nRowcAlStX5oMPPmDcuHFUqFCBb775hrFjx1qMkZ6eTv/+/SlbtizNmzcnPDzcfOPQokWL8ueff5Kenk6zZs2oUKECL774Iu7u7uYV7JJ3TIZh5PwVstySS5cu4e7uTlxcXJ7+pON+lZqayuLFi2nRokWWulMieUFzTAoDzXO5FzTPpKDS3M076WkZpCalk5KURkpSOi6e9jg422IYBrvWHMfBxZbQqr4FqiZ3XrhXcyzuTCJr5+wHk4mGT4bj7G5/1451vegdZ9nz5wnz6nGfEm6E1fDDw9cp2/6GYfDvvgv88/sJ0lPTsba1olbbUnj4/dc/NTmd1d/sJSUxjTJ1Aij1sO89OZeCKPNmoXaO+VtS4U7nemHLdwAkJSURHR1NSEgIDg53t4SKiNw7t/LeVjEcERERERGRAi41OR0ra5P5po5nYi6zd/1JUpLSSE1KJz3N8mZqlZoEUTzcE5PJRIUGxQpdwvxec/dxxKeEG2eOXuLQ1jNUjCh+z47t5u2AkWHgXdyF8Jr+ePo737C/yWQisIwX3kVd2P7bMdJTM3ArYplY2PdXLCmJaTh7OhBSscjdDL/Ay++EuYiI3D59gouIiIiIiBQwRobBxdMJnD12hTMxl7lwKoEqTYMICPUArt6QMks9bZMJOwdrbB1sLG5UqKT5vRFaxZczRy/x797zhNXwuyt15FOS0jj89xmsba0oXe1qqQXvYi480inslm72CeDkZkettiVJSUrHyvrqFzIZ6RnE/HOeo7vOAVChflHzNhGRB52Li0uO23799Vfq1at3D6ORe0GJcxERERERkQIgNTmd2ENxnDl2mbPHLpOabHkTsLgziebEuYefE9VbhWDnYIOtgzV2DjbY2FkpSZ6PvIo64xngzIWT8RzedoZydYvm2dipyekc3naG6O1nr5ZXsbGiRHlv82rnW02aZzKZTNhfs2J6/6ZTHNpytU57QKgHRYq73nnwIiIFxLZt23LcVqxYsXsXiNwzSpyLiIiIiIjch9JS00lJTMfJze7q85R0dqw6Zt5uY29NkeIuFAl0xSfQ1dwPwN7RBt8ShaMOcUESWtWXQ1tP4xOUNwnntNR0oref5fC2M6T9/xcpbkUcCavpj61D3t+M0mQygcmEja0VZesG5Pn4IiL3s9DQ0PwOQe4xJc5FRERERETuA0aGQdyZxP9fUX6FC7Hx+AS5Ur1lCACOrnYElPbAxcOeIoGuePg5WZRckfufT5Brnn2hcSbmMn8vjyE16erNJ108HQir6Y9/Sbe79suC8Jr+FC/jiZWVCUcXu5vvICIiUoApcS4iIiIiIpKPju09z+kjlzj375Us5VcSL6VgGIY5EVqlaYn8CFHySF4mtF087UlLScfJ3Z6w6n4ULe2B6R58keLsbn/XjyEiInI/UOJcRERERETkHklNTufi6QR8Av8r1XF87wXOHb8CXC2/4l3MhSLFXfAJulp+RXXJHzwpSWlEbz+Lg4stJcp737R/RnoGx/Ze4PK5JCrUv1pH19HVjtrtS+Huq18eiIiI3A1KnIuIiIiIiNwlGekZXDiVwNljVzh77DIXTyeCYdCkd3nzTRcDy3ldTZYHuigJWkicOnyJg5tPYe9sS/EynlhbW2XbLyPD4Pj+CxzcdIqESykABJb1xN3HCQBPf+d7FrOIiEhho8S5iIiIiIhIHjsTc5kjO89y7vgV0lMzLLY5e9iTeDnFnDgvFuaZHyFKPioW7sH+jbEkxadyfO8Fgq5bdW4YBicPXmT/xlPEX0wGwM7RhlJVfHHxdMiPkEVERAodJc5FRERERETuQHJiGuf+vYyHnzNObldvmJickMrpI5cAsHWwoUigCz7FXSkS5KKbKgpW1laEVPZhz58nOPT3GQLLepnrk8dfTGbLr0e4fD4JuDp/Sj3sQ4mHvLGxtc7PsEVEbio4OJhBgwYxaNCg/A5F5I5l/3swERERERERyVZ6WgZn/73MnnUn+WP2flbM+Ie/l8UQeyjO3McnyJXwWgE80qk0j/YuR5WmJQgs56WkuZgFlffC1sGGhLhkTl4zdxxcbElJTsfGzpqwGv5EdC9DqSq+SpqLSK5FRkZiMpkwmUzY2NgQFBTE888/z4ULF/I7tDwTHBxsPsfMR/HixfM9pkmTJuW6/+rVqy3id3R0pHz58nz++ed3L0i5JVpxLiIiIiIikguJV1LY8du/nD8ZT0aaZfkVV29HbOz/W5dk72RLaFXfex2iFCA2ttaEVCzC/o2x/L3sKAGl3DFZmbC2saJq8xI4e9hj56B/sovI7WnevDkzZ84kLS2N3bt307t3by5evMh3332X36HlmZEjR9KnTx/zc2vr2/+CMTU1FVtb27wI65bt27cPNzc3EhMTWbhwIc8//zylSpWicePG+RKP/EcrzkVERERERLKRmpxuviEjgJ2DjTlpfvWmjl5UbhJEk17lqN8ljKBy3jcYTSSrEg95Y2179Z/lJw5cNLd7+jsraS4id8Te3h5/f3+KFy9O06ZN6dy5M8uWLQMgPT2dp59+mpCQEBwdHQkPD2fy5MkW+0dGRtKuXTvef/99AgIC8Pb2pn///qSmppr7nD59mtatW+Po6EhISAjffPNNljhiYmJo27YtLi4uuLm50alTJ06dOmXePnz4cCpXrsyMGTMICgrCxcWF559/nvT0dMaPH4+/vz++vr6MHj06y9iurq74+/ubHz4+PuZtn376KaVKlcLOzo7w8HBmzZplsa/JZGLq1Km0bdsWZ2dnRo0aBcDChQupWrUqDg4OlCxZkhEjRpCWlmYRb1BQEPb29hQtWpSBAwcC0LBhQ44ePcrgwYPNK8hzy9fXF39/f0JCQhg4cCDBwcFs3brVvH3JkiU88sgjeHh44O3tTatWrTh06JB5e0pKCgMGDCAgIAAHBweCg4MZO3aseXtcXBx9+/bF19cXNzc3GjVqxPbt23MdX2Gmv4lFRERERESuEx+XzKZfjmAC6jweiq29NdY2VlRuEoSLpz0unva39I9ikezYOdhQ9bFgLp5KwLu4S36HIyK5lJyenOM2K6ywtbbNs7721va3GeV/Dh8+zJIlS8wrqjMyMihevDhz5syhSJEirFu3jr59+xIQEECnTp3M+61atYqAgABWrVrFwYMH6dy5M5UrVzav8o6MjOTYsWP89ttv2NnZMXDgQE6fPm3e3zAM2rVrh7OzM2vWrCEtLY1+/frRuXNnVq9ebe536NAhfv31V5YsWcKhQ4fo2LEj0dHRhIWFsWbNGtatW0fv3r1p3LgxtWrVuun5/vTTT7z44otMmjSJJk2asGjRInr16kXx4sWJiIgw9xs2bBhjx47lww8/xNramqVLl/LUU0/x0UcfUa9ePQ4dOkTfvn3NfefOncuHH37I999/T/ny5YmNjTUnoOfNm0elSpXo27evxSr4W2EYBkuXLuXYsWPUrFnT3B4fH8+QIUN46KGHiI+P55133qF9+/Zs27YNKysrPvroIxYsWMCcOXMICgri2LFjHDt2zDxmy5Yt8fLyYvHixbi7u/PZZ5/RuHFj9u/fj5eX123FWlgocS4iIiIiInKNc8evsGXJUVKT0nBwtiUpPhVb+6s//w4o5Z7P0cmDxifQFZ9A1/wOQ0RuwUurX8pxW3nv8jxf+Xnz86F/DCUlPSXbvqEeoQyqOsj8/J0/3yE+Nd6iz8eNP76tGBctWoSLiwvp6ekkJV292fAHH3wAgK2tLSNGjDD3DQkJYd26dcyZM8cice7p6cnHH3+MtbU1ZcqUoWXLlqxcuZI+ffqwf/9+fv31VzZs2GBO8k6fPp2yZcua91+xYgU7duwgOjqawMBAAGbNmkX58uXZtGkT1atXB64m8mfMmIGrqyvlypUjIiKCffv2sXjxYqysrAgPD2fcuHGsXr3aInH+2muv8dZbb5mfjxkzhoEDB/L+++8TGRlJv379ABgyZAgbNmzg/ffft0icd+vWjd69e5ufd+/enddff52ePXsCULJkSd59911effVVhg0bRkxMDP7+/jRp0gRbW1uCgoKoUaMGAF5eXlhbW5tXwd+KzNrsycnJZGRkMHLkSOrXr2/e/vjjj1v0nz59Or6+vuzevZsKFSoQExND6dKleeSRRzCZTJQoUcLcd9WqVezcuZPTp09jb3/1S5j333+f+fPnM3fuXPMXA5I9Jc5FRERERET+X8w/59j1+3GMDAN3XyeqtQjGwTl/ap6KiIjcroiICD799FMSEhL44osv2L9/Py+88IJ5+9SpU/niiy84evQoiYmJpKSkULlyZYsxypcvb1E3PCAggJ07dwKwZ88ebGxsqFatmnl7mTJl8PDwMD/fs2cPgYGB5qQ5QLly5fDw8GDPnj3mxHlwcDCurv99gejn54e1tTVWVlYWbdeuZgd45ZVXiIyMND8vUqSI+bjXJ4Tr1q2bpRzNtbEDbNmyhU2bNlmUhcn84iEhIYEnnniCSZMmUbJkSZo3b06LFi1o3bo1NjZ3ll79448/cHV1JTk5mY0bNzJgwAC8vLx4/vmrX8AcOnSIt99+mw0bNnD27FkyMq7eZyUmJoYKFSoQGRnJo48+Snh4OM2bN6dVq1Y0bdrUfE5XrlzB29uynFxiYqJFuRfJnhLnIiIiIiJS6GVkGOxdd5Lo7WcACCjtQaVGgVjb6LZQIiJiaWLDiTlus7rudoJj643NoWfWviPrjryzwK7h7OxMaGgoAB999BERERGMGDGCd999lzlz5jB48GAmTpxI7dq1cXV1ZcKECfz1118WY1x/s0yTyWRO2hqGYW7LiWEY2W6/vj2749zo2JmKFCliPsfrXX/c7GJxdna2eJ6RkcGIESPo0KFDlvEcHBwIDAxk3759LF++nBUrVtCvXz8mTJjAmjVr7ujGoiEhIeYvHMqXL89ff/3F6NGjzYnz1q1bExgYyLRp0yhatCgZGRlUqFCBlJSrv2SoUqUK0dHR/Prrr6xYsYJOnTrRpEkT5s6dS0ZGBgEBARalcTJd+yWHZE+JcxERERERKfT2bYg1J83DavoTWtVXNcxFRCRbt1J3/G71vVXDhg3jscce4/nnn+ePP/6gTp065lImwC2vPi5btixpaWls3rzZXK5k3759XLx40dynXLlyxMTEcOzYMfOq8927dxMXF2dR0iWvlS1blrVr19KjRw9z27p16256zCpVqrBv374ck/EAjo6OtGnThjZt2tC/f3/KlCnDzp07qVKlCnZ2dqSnp99x/NbW1iQmJgJw7tw59uzZw2effUa9evUAWLt2bZZ93Nzc6Ny5M507d6Zjx440b96c8+fPU6VKFWJjY7GxsSE4OPiOYytslDgXEREREZFCL6RSEU5FxxFeK0B1zEVE5IHTsGFDypcvz5gxYyhdujRfffUVS5cuJSQkhFmzZrFp0yZCQkJyPV5mWZA+ffrw+eefY2Njw6BBg3B0dDT3adKkCRUrVuTJJ59k0qRJ5puDNmjQIEuZlLz0yiuv0KlTJ6pUqULjxo1ZuHAh8+bNY8WKFTfc75133qFVq1YEBgbyxBNPYGVlxY4dO9i5cyejRo0iKiqK9PR0atasiZOTE7NmzcLR0dFcUzw4OJjff/+dLl26YG9vby4dczOnT58mKSnJXKpl1qxZdOzYEbhaZ97b25vPP/+cgIAAYmJieP311y32//DDDwkICKBy5cpYWVnxww8/4O/vj4eHB02aNKF27dq0a9eOcePGER4ezokTJ1i8eDHt2rW7q6/Dg0C/OxQRERERkUIp4dJ/N2tzcLalftdwJc1FROSBNWTIEKZNm0a7du3o0KEDnTt3pmbNmpw7d85i9XluzZw5k8DAQBo0aECHDh3o27cvvr6+5u0mk4n58+fj6elJ/fr1adKkCSVLlmT27Nl5eVpZtGvXjsmTJzNhwgTKly/PZ599xsyZM2nYsOEN92vWrBmLFi1i+fLlVK9enVq1avHBBx+YE+MeHh5MmzaNunXrUrFiRVauXMnChQvN9cNHjhzJkSNHKFWqFD4+PrmONzw8nICAAEJDQ3nttdd49tlnmTJlCgBWVlZ8//33bNmyhQoVKjB48GAmTJhgsb+Liwvjxo2jWrVqVK9enSNHjphvrGoymVi8eDH169end+/ehIWF0aVLF44cOYKfn98tXNXCyWRkFiWSO3bp0iXc3d2Ji4vDzc0tv8O561JTU1m8eDEtWrS4o1pOIjnRHJPCQPNc7gXNMymo7ubcjdl9jl1rjlOxUSDFwz3zdGwpOPT5KIXFnc71wpbvAEhKSiI6OpqQkBAcHBzyOxwRySO38t7WinMRERERESk0DMNg958n2LnqX4wMg3P/XsnvkERERETkPqTEuYiIiIiIFBpHdp4jetv/3wS0hj8VGxXP54hERETkQfTYY4/h4uKS7WPMmDH5HZ7kgm4OKiIiIiIihcLFUwns+fMEAGXrFqVk5dzXHxURERG5FV988QWJiYnZbvPy8rrH0cjtUOJcREREREQeeKnJ6fy97ChGhoFfSXdCKhXJ75BERETkAVasWLH8DkHukEq1iIiIiIjIA+/EwYskXErB0dWOihHFMZlM+R2SiIiIiNzHtOJcREREREQeeCXKe2NlbcLFwx47B/0zSERERERuTP/HKCIiIiIihUJgGdUTFREREZHcUakWERERERF5IKWlpLNz9b+kJKbldygiIiIiUsAocS4iIiIiIg8cwzDYufo4Mf+cY9Mv0RiGkd8hiYiIiEgBosS5iIiIiIg8cP7dc4ETBy6AyUTZOkV1M1ARERERuSVKnIuIiIiIyAPl0rlEdv1xHIDwmv54FXXO54hEREQebJGRkbRr1878vGHDhgwaNCjf4rlfBAcHM2nSpPwOQ26TEuciIiIiIvLASEtN5++lMWSkZVAkyJVSVXzyOyQREZF7LjY2lhdffJHQ0FAcHBzw8/PjkUceYerUqSQkJNz148+bN4933303T8e8PjmfyWQymR82NjYEBQUxZMgQkpOT8/T4NxIVFYWHh0eW9k2bNtG3b997Fsfq1astroejoyPly5fn888/v2cxPEhs8jsAERERERGRvPLP7ye4ciEJe2dbKjcJUokWEREpdA4fPkzdunXx8PBgzJgxPPTQQ6SlpbF//35mzJhB0aJFadOmTZb9UlNTsbW1zZMYvLy88mSc3Jo5cybNmzcnNTWV7du306tXL5ydnfM8eX+rfHzy5wv8ffv24ebmRmJiIgsXLuT555+nVKlSNG7cOF/iKai04lxERERERB4IKUlpnDtxBUwmHn40CHtHrRMSEZHCp1+/ftjY2LB582Y6depE2bJleeihh3j88cf55ZdfaN26NXB1pfbUqVNp27Ytzs7OjBo1ivT0dJ5++mlCQkJwdHQkPDycyZMnW4yfnp7OkCFD8PDwwNvbm1dffTXLTbivL9WSkpLCq6++SrFixXB2dqZmzZqsXr3avD1zxfbSpUspW7YsLi4uNG/enJMnTwIwfPhwvvzyS37++Wfzaupr9/fw8MDf35/AwEBatWpFmzZt2Lp1q0VMn376KaVKlcLOzo7w8HBmzZplsT0mJoa2bdvi4uKCm5sbnTp14tSpU+bt27dvJyIiAldXV9zc3KhatSqbN29m9erV9OrVi7i4OHNsw4cPB7KWajGZTHzxxRe0b98eJycnSpcuzYIFCyziWLBgAaVLl8bR0ZGIiAi+/PJLTCYTFy9ezPE1v56vry/+/v6EhIQwcOBAgoODLa7HkiVLeOSRR8yvYatWrTh06JDF6zVgwAACAgJwcHAgODiYsWPHmrfHxcXRt29ffH19cXNzo1GjRmzfvj3X8RUUSpyLiIiIiMgDwc7BhnqdwqjSrATexVzyOxwREXlAZSQn5/gwUlLytO+tOnfuHMuWLaN///44O2d/j49rf401bNgw2rZty86dO+nduzcZGRkUL16cOXPmsHv3bt555x3eeOMN5syZY95n4sSJzJgxg+nTp7N27VrOnz/PTz/9dMO4evXqxZ9//sn333/Pjh07eOKJJ2jevDkHDhww90lISOD9999n1qxZ/P7778TExPDyyy8D8PLLL9OpUydzMv3kyZPUqVMn22Pt37+fVatWUbNmTXPbTz/9xIsvvshLL73Erl27ePbZZ+nVqxerVq0CwDAM2rVrx/nz51mzZg3Lly/n0KFDdO7c2TzGk08+SfHixdm0aRNbtmzh9ddfx9bWljp16jBp0iTc3NzMsWXGnZ0RI0bQqVMnduzYQYsWLXjyySc5f/48AEeOHKFjx460a9eObdu28eyzz/Lmm2/e8NreiGEYLFmyhGPHjllcj/j4eIYMGcKmTZtYuXIlVlZWtG/fnoyMDAA++ugjFixYwJw5c9i3bx9ff/01wcHB5jFbtmxJbGwsixcvZsuWLVSpUoXGjRubz+NBoSUYIiIiIiLywLC1tyaglHt+hyEiIg+w4y8OynGbQ4UK+Azob35+4pVXsyTIM9mXLo3vS0PMz0+++RYZV65Y9Amc+uktxXbw4EEMwyA8PNyivUiRIiQlJQHQv39/xo0bB0C3bt3o3bu3Rd8RI0aY/xwSEsK6deuYM2cOnTp1AmDSpEkMHTqUxx9/HICpU6eydOnSHGM6dOgQ3333Hf/++y9FixYFribClyxZwsyZMxkzZgxwtVTM1KlTKVWqFAADBgxg5MiRALi4uODo6EhycjL+/v5ZjtG1a1esra1JS0sjOTmZVq1aMXToUPP2999/n8jISPr16wfAkCFD2LBhA++//z4RERGsWLGCHTt2EB0dTWBgIACzZs2ifPnybNq0ierVqxMTE8Mrr7xCmTJlAChdurR5fHd3d0wmU7axXS8yMpKuXbsCMGbMGKZMmcLGjRtp3rw5U6dOJTw8nAkTJgAQHh7Orl27GD169E3HvVbx4sUBSE5OJiMjg5EjR1K/fn3z9szXLtP06dPx9fVl9+7dVKhQgZiYGEqXLs0jjzyCyWSiRIkS5r6rVq1i586dnD59Gnt7e/P1nT9/PnPnzr2nNd3vNq04FxERERGRAi3hUgqxh+NIik/N71BERETuC9ff42Pjxo1s27aN8uXLW9w0s1q1aln2nTp1KtWqVcPHxwcXFxemTZtGTEwMcLVEx8mTJ6ldu7a5v42NTbbjZNq6dSuGYRAWFoaLi4v5sWbNGovyIE5OTuakOUBAQACnT5/O1fl++OGHbNu2je3bt7No0SL2799P9+7dzdv37NlD3bp1LfapW7cue/bsMW8PDAw0J80BypUrh4eHh7nPkCFDeOaZZ2jSpAnvvfeeRey3omLFiuY/Ozs74+rqaj7Pffv2Ub16dYv+NWrUuOVj/PHHH2zbto1t27bxxRdfMGbMGD799L8vYQ4dOkS3bt0oWbIkbm5uhISEAJhf58jISLZt20Z4eDgDBw5k2bJl5n23bNnClStX8Pb2tng9o6Ojb/ua3K+04lxERERERAq000cu8c8fx/EJcqVG65L5HY6IiDzgik2elOO26xPWRSeMz3XfgNGj7igugNDQUEwmE3v37rVoL1ny6t+Pjo6OFu3Xl3OZM2cOgwcPZuLEidSuXRtXV1cmTJjAX3/9ddsxZWRkYG1tzZYtW7C2trbY5uLyX2m1629MajKZstROz4m/vz+hoaHA1VXaly9fpmvXrowaNcrcfv31NgzD3Hbtn3PqM3z4cLp168Yvv/zCr7/+yrBhw/j+++9p3759rmK80XlmlkjJLo7cXoNrhYSE4OHhAUD58uX566+/GD16NM8//zwArVu3JjAwkGnTplG0aFEyMjKoUKECKf//64gqVaoQHR3Nr7/+yooVK+jUqRNNmjRh7ty5ZGRkEBAQYFFjPlPmMR8USpyLiIiIiEiBduFUAgCe/tnXchUREclLVv9fniI/++bE29ubRx99lI8//pgXXnghxzrnOfnjjz+oU6eOuaQJYLGK2N3dnYCAADZs2GAu/ZGWlmauc52dhx9+mPT0dE6fPk29evVu46yusrOzIz09PVd9MxP0iYmJAJQtW5a1a9fSo0cPc59169ZRtmxZ4Orq8piYGI4dO2Zedb57927i4uLMfQDCwsIICwtj8ODBdO3alZkzZ9K+fftbiu1GypQpw+LFiy3aNm/efMfjWltbm6/FuXPn2LNnD5999pn59Vi7dm2Wfdzc3OjcuTOdO3emY8eONG/enPPnz1OlShViY2OxsbEx1z1/UKlUi4iIiIiIFGgXY+MB8PBzyudIRERE8t8nn3xCWloa1apVY/bs2ezZs8d8g8e9e/dmWfV9rdDQUDZv3szSpUvZv38/b7/9Nps2bbLo8+KLL/Lee+/x008/sXfvXvr168fFixdzHDMsLIwnn3ySHj16MG/ePKKjo9m0aRPjxo3LkiS+keDgYHbs2MG+ffs4e/Ysqan/lWi7ePEisbGxnDhxgjVr1jBy5EjCwsLMSe9XXnmFqKgopk6dyoEDB/jggw+YN2+e+SaeTZo0oWLFijz55JNs3bqVjRs30qNHDxo0aEC1atVITExkwIABrF69mqNHj/Lnn3+yadMm8/jBwcFcuXKFlStXcvbsWRISEnJ9Xtd69tln2bt3L6+99hr79+9nzpw5REVFAVlXzN/I6dOniY2N5ejRo/zwww/MmjWLtm3bAuDp6Ym3tzeff/45Bw8e5LfffmPIkCEW+3/44Yd8//337N27l/379/PDDz/g7++Ph4cHTZo0oXbt2rRr146lS5dy5MgR1q1bx1tvvZUnSf77iRLnIiIiIiJSYCUnppFwKQVMJiXORUREgFKlSvH333/TpEkThg4dSqVKlahWrRpTpkzh5Zdf5t13381x3+eee44OHTrQuXNnatasyblz5yxWnwO89NJL9OjRg8jISHM5l5uVK5k5cyY9evTgpZdeIjw8nDZt2vDXX39Z1BS/mT59+hAeHm6uv/7nn3+at/Xq1YuAgACKFy9O165dKV++PL/++is2NleLbbRr147JkyczYcIEypcvz2effcbMmTNp2LAhcDUpPX/+fDw9Palfvz5NmjShZMmSzJ49G7i6YvvcuXP06NGDsLAwOnXqxGOPPWa+kWqdOnV47rnn6Ny5Mz4+Powfn3OJnhsJCQlh7ty5zJs3j4oVK/Lpp5/y5ptvAphvxJkb4eHhBAQEEBoaymuvvcazzz7LlClTALCysuL7779ny5YtVKhQgcGDB5tvRprJxcWFcePGUa1aNapXr86RI0dYvHgxVlZWmEwmFi9eTP369enduzdhYWF06dKFI0eO4Ofnd1vnfb8yGbdTKEeydenSJdzd3YmLi8PNzS2/w7nrUlNTWbx4MS1atMhSn0kkL2iOSWGgeS73guaZFFS5mbunoi+xeXE0Lp4ONOgWfo8jlIJOn49SWNzpXC9s+Q6ApKQkoqOjCQkJwcHBIb/DkUJs9OjRTJ06lWPHjuV3KA+EW3lvq8a5iIiIiIgUWBdO/X+ZFn+tNhcREZGC75NPPqF69ep4e3vz559/MmHCBAYMGJDfYRVKKtUiIiIiIiIF1sXYzBuDKnEuIiIiBd+BAwdo27Yt5cqV49133+Wll15i+PDhADz22GO4uLhk+xgzZkz+Bv4A0opzEREREREpsB6KKM6F2AS8iznndygiIiIid+zDDz/kww8/zHbbF198QWJiYrbbvLy87mZYhZIS5yIiIiIiUmA5u9vj7J77m2WJiIiIFFTFihXL7xAKFZVqERERERERERERERG5hlaci4iIiIhIgXRk51ky0gz8S7nj5GaX3+GIiIiIyANEiXMRERERESmQjuw8R/yFJJw97ZU4FxEREZE8pVItIiIiIiJS4KQkpRF/IQkATz+nfI5GRERERB40SpyLiIiIiEiBc/FUAgBO7vbYOeqHtCIiIiKSt5Q4FxERERGRAiczce7pr9XmIiIiBU3Dhg0ZNGhQfochckNKnIuIiIiISIFzIfZq4txDZVpEREQsREZGYjKZeO+99yza58+fj8lkyqeobl/Dhg0xmUyYTCasrKzw8/PjiSee4OjRo/kdmjzglDgXEREREZECxTAMLp7WinMREZGcODg4MG7cOC5cuHBPj5uamnpXxu3Tpw8nT57k+PHj/Pzzzxw7doynnnrqrhxLJJMS5yIiIiIiUqAkXk4lPTUDKxsrXL0d8zscERGR+06TJk3w9/dn7NixOfZZt24d9evXx9HRkcDAQAYOHEh8fLx5u8lkYv78+Rb7eHh4EBUVBcCRI0cwmUzMmTOHhg0b4uDgwNdff825c+fo2rUrxYsXx8nJiYceeojvvvvujs7HyckJf39/AgICqFWrFv3792fr1q3m7enp6Tz99NOEhITg6OhIeHg4kydPthhj9erV1KhRA2dnZzw8PKhbt67FqvWFCxdStWpVHBwcKFmyJCNGjCAtLe2O4paCLV8T57///jutW7emaNGi2b4Z582bR7NmzShSpAgmk4lt27ZlGSM5OZkXXniBIkWK4OzsTJs2bfj3338t+ly4cIHu3bvj7u6Ou7s73bt35+LFixZ9YmJiaN26Nc7OzhQpUoSBAweSkpKSx2csIiIiIiJ3ysnNjmZ9KlC3YyhWVgXvJ+ciIlKwpaWm5/hIT8vI0763y9ramjFjxjBlypQseTKAnTt30qxZMzp06MCOHTuYPXs2a9euZcCAAbd8rNdee42BAweyZ88emjVrRlJSElWrVmXRokXs2rWLvn370r17d/7666/bPp9rnT9/nh9++IGaNWua2zIyMihevDhz5sxh9+7dvPPOO7zxxhvMmTMHgLS0NNq1a0eDBg3YsWMH69evp2/fvubSNUuXLuWpp55i4MCB7N69m88++4yoqChGjx6dJzFLwZSvt5+Pj4+nUqVK9OrVi8cffzzb7XXr1uWJJ56gT58+2Y4xaNAgFi5cyPfff4+3tzcvvfQSrVq1YsuWLVhbWwPQrVs3/v33X5YsWQJgfsMuXLgQuPqtVMuWLfHx8WHt2rWcO3eOnj17YhgGU6ZMuUtnLyIiIiIit8vaxgo3rTYXEZF8sPTzXTlu8ynhRo1WIebnK2bszpIgz+RV1IXa7UuZn//21V5SkyxXOLfsX+m242zfvj2VK1dm2LBhTJ8+3WLbhAkT6Natm/kGnaVLl+ajjz6iQYMGfPrppzg4OOT6OIMGDaJDhw4WbS+//LL5zy+88AJLlizJkuy+FZ988glffPEFhmGQkJBAWFgYS5cuNW+3tbVlxIgR5uchISGsW7eOOXPm0KlTJy5dukRcXBytWrWiVKmr17xs2bLm/qNHj+b111+nZ8+eAJQsWZJ3332XV199lWHDht1WzFLw5Wvi/LHHHuOxxx7LcXv37t2Bqz/9yE5cXBzTp09n1qxZNGnSBICvv/6awMBAVqxYQbNmzdizZw9Llixhw4YN5jfntGnTqF27Nvv27SM8PJxly5axe/dujh07RtGiRQGYOHEikZGRjB49Gjc3tzw8axERERERERERkbtv3LhxNGrUiJdeesmifcuWLRw8eJBvvvnG3GYYBhkZGURHR1sklW+mWrVqFs/T09N57733mD17NsePHyc5OZnk5GScnZ1v+zyefPJJ3nzzTQBOnTrFmDFjaNq0KVu2bMHV1RWAqVOn8sUXX3D06FESExNJSUmhcuXKAHh5eREZGUmzZs149NFHadKkCZ06dSIgIMB8PTZt2mSxwjw9PZ2kpCQSEhJwctI9VQqjfE2c36ktW7aQmppK06ZNzW1FixalQoUKrFu3jmbNmrF+/Xrc3d0tvtGqVasW7u7urFu3jvDwcNavX0+FChXMSXOAZs2akZyczJYtW4iIiMj2+Jlv/EyXLl0Crt4I4W7dDOF+knmOheFcJX9ojklhoHku94LmmRRU2c3dtJR0Ni48goevI2XqBGBlrVItcvv0+SiFxZ3Odb1HLDXrWyHHbZmlPzI16V0u130b9ShzZ4Flo379+jRr1ow33niDyMhIc3tGRgbPPvssAwcOzLJPUFCQOT7DMCy2ZTcXrk+IT5w4kQ8//JBJkybx0EMP4ezszKBBg+6oJLK7uzuhoaEAhIaGMn36dAICApg9ezbPPPMMc+bMYfDgwUycOJHatWvj6urKhAkTLMrDzJw5k4EDB7JkyRJmz57NW2+9xfLly6lVqxYZGRmMGDEiy8p54JZW38uDpUAnzmNjY7Gzs8PT09Oi3c/Pj9jYWHMfX1/fLPv6+vpa9PHz87PY7unpiZ2dnblPdsaOHWvxM5BMy5YtK1TfRC1fvjy/Q5AHnOaYFAaa53IvaJ5JQXXt3E25ZMWl/fZYHczgyOVt+ReUPFD0+SiFxe3O9YSEhDyOpGCzsbXO97634r333qNy5cqEhYWZ26pUqcI///xjTkZnx8fHh5MnT5qfHzhwIFdz4Y8//qBt27Y89dRTwNUk/YEDB25pFfvNZJZnTkxMNB+zTp069OvXz9zn0KFDWfZ7+OGHefjhhxk6dCi1a9fm22+/pVatWlSpUoV9+/bd8HpI4VOgE+c5MQzD4lu767/Bu90+1xs6dChDhgwxP7906RKBgYE0bdq0UJR3SU1NZfny5Tz66KPY2trmdzjyANIck8JA81zuBc0zKaiym7uHtp7hQNxp/Eu5UblJYD5HKAWdPh+lsLjTuZ75C3spmB566CGefPJJi/v4vfbaa9SqVYv+/fvTp08fnJ2d2bNnD8uXLzf3a9SoER9//LF5RfZrr72Wq/kTGhrKjz/+yLp16/D09OSDDz4gNjb2jhLnCQkJ5sWtp06dYtSoUTg4OJirUISGhvLVV1+xdOlSQkJCmDVrFps2bSIk5Gq9+ejoaD7//HPatGlD0aJF2bdvH/v376dHjx4AvPPOO7Rq1YrAwECeeOIJrKys2LFjBzt37mTUqFG3HbcUbAU6ce7v709KSgoXLlywWHV++vRp6tSpY+5z6tSpLPueOXPGvMrc398/y519L1y4QGpqapaV6Neyt7fH3t4+S7utrW2h+p+uwna+cu9pjklhoHku94LmmRRU187dy2eTsTKZKFLUVfO5MEtPhYRzEH8W4s/8/5/P/P/jLGSkQckIKNMC7G5eU1efj1JY3O5c1/uj4Hv33XeZM2eO+XnFihVZs2YNb775JvXq1cMwDEqVKkXnzp3NfSZOnEivXr2oX78+RYsWZfLkyWzZsuWmx3r77beJjo6mWbNmODk50bdvX9q1a0dcXNxtxz9t2jSmTZsGXK0SUbFiRRYvXkx4eDgAzz33HNu2baNz586YTCa6du1Kv379+PXXXwFwcnJi7969fPnll5w7d46AgAAGDBjAs88+C1wt2bxo0SJGjhzJ+PHjsbW1pUyZMjzzzDO3HbMUfAU6cV61alVsbW1Zvnw5nTp1AuDkyZPs2rWL8ePHA1C7dm3i4uLYuHEjNWrUAOCvv/4iLi7OnFyvXbs2o0eP5uTJk+abAixbtgx7e3uqVq2aD2cmIiIiIiLXMwyDC7FXfyLu4Vd4SiMWSinx/58Uz0yMn/0vKZ5wDhIvAsaNx9g1F/b/CmVaQXgLsFWNWhEpHKKiorK0lShRgqSkJIu26tWrs2zZshzHKVq0KEuXLrVou3jxovnPwcHBWWqgw9Ubcc6fP/+GMa5evfqG22+1r729PTNnzmTmzJkW7WPHjgWulnX+6aefbjhGs2bNaNasWa7jkgdfvibOr1y5wsGDB83Po6Oj2bZtG15eXgQFBXH+/HliYmI4ceIEAPv27QOurhD39/fH3d2dp59+mpdeeglvb2+8vLx4+eWXeeihh2jSpAkAZcuWpXnz5vTp04fPPvsMgL59+9KqVSvzt1JNmzalXLlydO/enQkTJnD+/Hlefvll+vTpUyhKroiIiIiIFAQJl1JITUrDZGXCzccxv8OR22UYkHghm6T4uf/+nJp483GsbcGpCDj7/P/D++p/nYpcHX/Xj3DpOOyYDfsWQ7m2ULop2GT91bCIiIjI9fI1cb5582YiIiLMzzPrhffs2ZOoqCgWLFhAr169zNu7dOkCwLBhwxg+fDgAH374ITY2NnTq1InExEQaN25MVFSU+SYBAN988w0DBw401z1q06YNH3/8sXm7tbU1v/zyC/369aNu3bo4OjrSrVs33n///bt27iIiIiIicmsyV5u7+zphbW2Vz9FIjtJS/iudknD2v5XjmUnxhPNXS6ncjJ3L/yfEi/z/4/+T4pnP7d3gBvekIqg2HF0LO+fClVPw99ewZxGUbwelGoONXZ6dsoiI3J4//vjj/9i77/Aoyu2B49/dTbLpvfceQui9SFNUOghSFIUIgoqKiAp6FSki6kUElWu5KOVy/V1QVESaFKVJ7wFCgJCQAAkJIaTX3f39MbCw1AAhm5DzeZ59sjvzzjvvbCbJ5syZ89K1a9ebrs/Pz6/C0QhhyqyB844dO97wlo7LYmNjiY2NvWUf1tbWfPnllyYTHFzL1dWV//73v7fsJzAwkOXLl9+yjRBCCCGEEMJ8DHoDNg5WuEiZluor+W/Y/tXtA+MqNdi43iAoflXW+L2WVlGrIaQ9BLWFpI1KBnrBedgzH+J/h5i+ENj23vYhhBDinjRr1oz9+/ebexhC3FCNrnEuhBBCCCGEqD0Col0JiHZFr79NbWthHroy2P+DEjTXWF2VLe4Btm5Xntt5gI0LqDW377MyqDUQ9jAEt4eTf8GhX5SM+F1z0Bz6Bc98H8htDC7+SrBdCCFElbGxsSE8PNzcwxDihiRwLoQQQgghhKhR1OpblOcQ5pP4pxKQtnGBnl9Uv1IoGguIeBRCOkDiejj8KxRkEpJ5GM2qg2CpBadAcA4A50BwDlKeWzuZe+RCCCGEMAMJnAshhBBCCCGqPZ1Oj1qtQnWrmtbCfMpLlUA0KCVQqlvQ/GoWVhDVFcIeRh+/ivxN/4enxlLJmL+QqDyuZu18KZgeBF51wbfJrWurCyGEEOKBIIFzIYQQQgghRLWXfPA8iXsyCGnkQUQzL3MPR1zrxFooylZqk4d1MvdoKsZCi6FOdw6fVBHUtQvq4gtwMeXS45TyNT8Dii9C+kVIj4Ojy6HZcIh8zNyjF0IIIcR9JoFzIYQQQgghRLWXnV5IWYkOtUYyfaudsmI4vFR5Xu8J0FiadTh3RaUGRx/lEdjyyvKyYshJVYLo5w7Bqa2wbyH4NAQHuYAjhBBCPMhk5hMhhBBCCCFEtWYwGLiYXgiAs5etmUcjrnN8DZTkKpN+hnQ092gql6U1uEdA+CPQZjR41gVdKez4GgwySa0QQgjxIJPAuRBCCCGEEKLKlJXoKC0uv6NtivPLKCksQ6VW4ewhgfNqpawIjvymPK//pDIB54NKpYJWL4GFFjLi4dhqc49ICCEqzaRJk2jUqJG5hyFEtSKBcyGEEEIIIcR9p9fpObkvk/ULjrB50TF05foKb3vxXBEAjm42aCzlX5hqJWEVlOaDgzcEtzP3aO4/e09o/KzyfP8PkJdu3vEIIcQtbN26FY1GQ5cuXe5L/8HBwahUysTdGo0GX19fhg8fTnZ29n3Z341s2LABlUrFxYsXK7xNbGyscdwqlQo3Nze6dOnCwYMH799ARY0knzqFEEIIIYQQ91V2egFbfjpB/Naz6Mr0RLf1RWNR8X9FcjKUwLmzt2SbVyulBcpkmQD1ngS1xrzjqSrhncGrHujKYNu/QF/xi0BCCFGV5s6dy6uvvsqWLVtISUm5L/uYMmUKaWlppKSk8MMPP7Bp0yZGjx59X/ZVmbp06UJaWhppaWmsX78eCwsLevToYe5hiWpGAudCCCGEEEKI+6K0uJy4DafZ+vMJ8rKKsLS2oEGnAHzCne6on4vnlPrmLlLfvHpJWKUEzx39IKituUdTdVQqaPmiUrLl/DE4tsrcIxJCiOsUFBTw448/8tJLL9GjRw/mz59vsv7jjz/Gy8sLBwcHhg8fTnFxscn6Xbt28eijj+Lu7o6TkxMdOnRg79691+3HwcEBb29v/Pz86NSpE0OGDLmu3c8//0xMTAxarZbg4GBmzJhhsj47O5shQ4bg4uKCra0tXbt25fjx48b1p06domfPnri4uGBnZ0dMTAwrV64kOTmZTp06AeDi4oJKpSI2NrZC749Wq8Xb2xtvb28aNWrE+PHjSU1NJTMz09hm/PjxREZGYmtrS2hoKBMmTKCsrMy4/sCBA3Tq1AkHBwccHR1p2rQpu3fvNq7funUr7du3x8bGhoCAAEaPHk1BQUGFxieqBwmcCyGEEEIIISrd+dN5bPy/BFIOZwHgX8eVjk9HEVDXFZVKBcDFjEJ2rUiivEx3y77cA+1x87eXjPPqpCT/SrZ5/f6grmX/Wtp7QJMhyvMD/4Pcs+YdjxCiSpWXlt70oSsvq9S2d2vx4sVERUURFRXFM888w7x58zBcmtT4xx9/ZOLEiXz44Yfs3r0bHx8fvvrqK5Pt8/LyGDp0KJs3b2b79u1ERETQrVs38vLybrrPM2fOsHz5clq2bGlctmfPHgYMGMCgQYOIi4tj0qRJTJgwwSSQHxsby+7du1m2bBnbtm3DYDDQrVs3Y5D65ZdfpqSkhE2bNhEXF8cnn3yCvb09AQEB/PzzzwAkJCSQlpbG559/fsfvVX5+Pj/88APh4eG4ubkZlzs4ODB//nyOHDnC559/zpw5c5g5c6Zx/eDBg/H392fXrl3s2bOHt99+G0tLSwDi4uJ4/PHH6du3LwcPHmTx4sVs2bKFV1555Y7HJ8znAZ65RQghhBBCCGEuNvZWlJfqsXexpl4HP9z87E3W63V69q4+RVFeKXv/SKFZ1yDUmhsHX8Obehr/ERXVxNHlysSgzoEQ2MrcozGPsEcgZQekH4TtX0HnKbXvAoIQtdTvMz+66Tqv0Aja9H/a+Hrl7E/RlZXdsK17QBDtno41vv7jm88pLSo0afPE+Il3Ncbvv/+eZ555BlDKkuTn57N+/Xo6d+7MrFmzGDZsGM8//zwAU6dOZd26dSZZ5w8//LBJf99++y0uLi5s3LjRpKTJ+PHjee+999DpdBQXF9OyZUs+++wz4/rPPvuMRx55hAkTJgAQGRnJkSNHmD59OrGxsRw/fpxly5bx999/06ZNGwB++OEHAgICWLp0Kf379yclJYV+/fpRv359AEJDQ439u7q6AuDp6Ymzs3OF35/ly5djb698NikoKMDHx4fly5ejvur3+HvvvWd8HhwczBtvvMHixYsZN24cACkpKbz11lvUqVMHgIiICGP76dOn8/TTTzNmzBjjui+++IIOHTrw9ddfY21tXeGxCvORv+pCCCGEEEKIe6Yr15OelGN8beespWWvUNoNjLguaA6g1qhp/Fggags1madyidtwxpgJJ6q54lxIWKk8r99fKV1SG10u2WJpA+ePQ8KKe+puf+pFLhbefXapEEJclpCQwM6dOxk0aBAAFhYWDBw4kLlz5wIQHx9P69atTba59nVGRgYvvvgikZGRODk54eTkRH5+/nW10t966y3279/PwYMHWb9+PQDdu3dHp9MZ99W2rWk5r7Zt23L8+HF0Oh3x8fFYWFiYZKm7ubkRFRVFfHw8AKNHj2bq1Km0bduWiRMnVsoknp06dWL//v3s37+fHTt28Nhjj9G1a1dOnTplbLNkyRIeeughvL29sbe3Z8KECSbHP3bsWJ5//nk6d+7Mxx9/TGJionHdnj17mD9/Pvb29sbH448/jl6vJykp6Z7HL6qGZJwLIYQQQggh7klmSh6HNp2hMKeENv3CcfG2A8DV1+6W27l429HksSB2r0rm9NELWNtZENXKx6RNeaGK0uJyyTivTuJ/h/IScAkG/+bmHo152bkpJVt2fAsHFoFvY3Dyv+NusgtK+XZjIioVdKvvw2N1vbG6gwl0a53SAlBbgoWVuUciaqmer79z03UqtenFxG6vvFnhto+/+Nq9DeyS77//nvLycvz8/IzLDAYDlpaWZGdnV6iP2NhYMjMzmTVrFkFBQWi1Wlq3bk3pNeVj3N3dCQ8PB5Ss6lmzZtG6dWv++usvOnfujMFgMJZou3osN3p+bZvL2z3//PM8/vjjrFixgjVr1vDRRx8xY8YMXn311Qody43Y2dkZxw3QtGlTnJycmDNnDlOnTmX79u0MGjSIyZMn8/jjj+Pk5MSiRYtM6rNPmjSJp59+mhUrVrBq1SomTpzIokWLeOKJJ9Dr9bzwwgs3nCg1MDDwrsctqpYEzoUQQgghhBB3pbigjCNbzpJ24iIAWjtLysv0d9SHV4gjDTr6c/CvVE7syUBra0lwA3fj+txEK/5ckEDbvhG3DcSLKlB0EY6tVp43GFB7s82vFtpJKdmSth+2fw2PTgG15rabGQwGdiZdoEmQC8XlOvxdbDiZWcCve8+w6Vgm/ZsF0CzI5bqAU42SfgiKssHRV5lE1vIuShPoyuHiKcg6oWT2nz8G+edAbaFcvHGPvPKwc7ttd0JUBguril+0uV9tb6a8vJz//Oc/zJgxg8cee8xkXb9+/fjhhx+Ijo5m+/btDBkyxLhu+/btJm03b97MV199Rbdu3QBITU3l/Pnzt92/RqP8/isqKgKgbt26bNmyxaTN1q1biYyMRKPRULduXcrLy9mxY4exVEtWVhbHjh0jOjrauE1AQAAvvvgiL774Iu+88w5z5szh1VdfxerSe3Y5w/1uqVQq1Gq1cdx///03QUFBvPvuu8Y2V2ejXxYZGUlkZCSvv/46Tz31FPPmzeOJJ56gSZMmHD582CQ4L2oeCZwLIYQQQggh7ohBb+DUoSwSdqRTXqoDlYqQBu5EtvDCwur2AcNrBdR1pbiwjGM70jm85Sx2zlo8Ah0oLihDX6Jk3Tq6Sy3QauHIb6ArBbdw8G1i7tFUDyoVtBgJK99Ugrvxv0NMn9tudvB0Dv/edBIvJ2um9q7HP7pFs/3kBZbsOU1WfinfbEgkwsuBp1oEEORWAy8aZSbAnx+YLrNxvRREvxRIv/zV1vXKRZjCC1cC5FnH4cJJ0N2gPrS+XHm/s05cKR1k4wruEVcC6a4hoJG7VUTtsnz5crKzsxk+fDhOTk4m65588km+//573n77bYYOHUqzZs146KGH+OGHHzh8+LBJ7fDw8HAWLlxIs2bNyM3N5a233sLGxua6/eXl5ZGeno7BYCA1NZVx48bh7u5uDIK/8cYbNG/enA8++ICBAweybds2Zs+ebZyMNCIigt69ezNixAi+/fZbHBwcePvtt/Hz86N3794AjBkzhq5duxIZGUl2djZ//vmnMageFBSESqVi+fLldOvWDRsbG2Pt8lspKSkhPT0dgOzsbGbPnk1+fj49e/Y0Hn9KSgqLFi2iefPmrFixgl9//dW4fVFREW+99RZPPvkkISEhnD59ml27dtGvXz9Aqf3eqlUrXn75ZUaMGIGdnR3x8fGsXbuWL7/8smLfTGF2EjgXQgghhBBC3JFdK5LITMkDwMnTlvod/XHyuP6f6TsR3tSTkoIyCnNLcfGxBSAnQ8n6cnDV3j4gn5kAFtbgEnRP4xC3UHgBTqxVntfm2uY3YucGTYcqGedxP4FfU3AOuGlzg8HA8oNnAWgU4Iz6UrmG1mFuNA505o/D6ayKS+f4uTw+WnmU6f0b4GBdgwLAunLYOUd5buehXGwpzoGiC8rj3CHT9hZacPCBkjwozLq+P0vbS8HwCOWijVuYUq7l/LFLj+NwMUXpO3WH8gDJShe10vfff0/nzp2vC5qDknE+bdo0IiIieP/99xk/fjzFxcX069ePl156iT/++MPYdu7cuYwcOZLGjRsTGBjItGnTePPN68vOvP/++7z//vsAeHh40Lx5c9auXYubm/Kz1qRJE3788Ufef/99PvjgA3x8fJgyZQqxsbHGPubNm8drr71Gjx49KC0tpX379qxcudJYpk2n0/Hyyy9z+vRpHB0d6dKlCzNnzgTAz8+PyZMn8/bbb/Pcc88xZMgQ5s+ff9v3afXq1fj4KOXhHBwcqFOnDj/99BMdO3YEoHfv3rz++uu88sorlJSU0L17dyZMmMCkSZMAJbM+KyuLIUOGcO7cOdzd3enbty+TJ08GoEGDBmzcuJF3332Xdu3aYTAYCAsLY+DAgbcdm6g+VAaZgafS5Obm4uTkRE5ODo6OjuYezn1XVlbGypUr6datm9ScFPeFnGOiNpDzXFQFOc9EZUuNv8CRv88S1dKboBi362q03i2D3oDBYECtUbLMD20+zY41R2jaMYpGj9wiIF54AZa9qgRyH/tQguf3y+65cOwPJfj46JQHInBeqb8fDQbY+E84uxdcw+CxD25asuXw2Rw+W3MMS42aT55sgJPN9fu+UFDKz3tO42JnxZNNr9RN1+kNaCrpZ+6+if8d9v0XrOyh5yzQOkBJPuSlQe4ZyD175WveOTBcXWJBpfwMu4VfyR538Ln9+VZWDBcSrwTSzx9TAvHXsnUzzUp3Ca4VWen3eq7XtngHQHFxMUlJSYSEhGBtLXc9CfGguJOfbck4F0IIIYQQQtyUwWAgLTEHjYUar2AlWOJfxwXPYEe0NpX774RKrUKFyrjfpANKLVUnz9tks2fEK2UbALbMhC4fgeW9ZcCLaxRkwYn1yvMGAx+IoHmlU6mgxQhY8YYSwD3yG9Tre8Omyw+mAdA+0uOGQXMAVzsrRrQPNZk4L+l8Ad9uTOTJpv40ra71zwuylKx7gMaDlaA5gNYetBFK0PpqunIoyFCC6Ja24Bp6d7XQLa3BK0Z5gHIhI//c9VnphVmQkgUpl+o5qy2Uki5XZ6Xbut7dsQshhHigSOBcCCGEEEIIcUMFOSUc3nSGzJQ8rO0scXs6CgsrDSqVqtKD5tc6sSfD+NzZy/bWjc8nXHmelwY7/w1tRktwtzId/kW5OOFZF7zrmXs01ZetKzR7Drb9Cw79DIGtlDreV0lIz+NYeh4atYou9bxv2+XVwfGVcWlk5pXw9YZEIr0deKp5IIFut/n5qGp750N5iRKADu10+/Yaiyt1z28jt7iMpMwCkrMKcLC2oL6fMx4O2hs3VqnAwVt5hLRXlt0sK/38ceXBCqVdLc1KF+JBkJKSQt26dW+6/siRIwQGBlbhiERNJoFzIYQQQgghhAmdTs/JfZmc2JOBvlyPSq0iIMa10kqyVERAHVdOH73AhQI9ds5Wt26ceSlwHtUNjq2GU1vBMwYiOt//gdYGeefg5AbleYMBZh1KjRDcDpL/hrT9cPhXaP2yyerLtc0finDH1e425/Y1hj8Ugr+LDavi0jmWnseU5YdpG+5O38b+ONlWg8Dumb2QuhNUakqbDOfbP0+QXViGt5MWX2cbutf3ueMs+YuFpfy0+zQnz+eTkVtyzdoUOtXx5JlWFSzPVJGs9OxTkpUuRA3m6+vL/v37b7leiIqSwLkQQgghhBDCKOtMPnEbz1CQXQyAm7899dr7Y+9yk6zO+8Ta3pK2A8LIWXns1oG20kIl0AUQ3RNsXGD/D7BnnjKBoGtI1Qz4QZVzGjZ8pGSbe9cHz2hzj6j6U6mUCwxp+yF5C9R7Ehy8ACgt16NRq1CrVXSr73PHXVtbaujdyI92ER4s2ZPKjpMX2HL8PLuSL9C/aQCd6nhW8sHcgfISpQ4+QFQ3Vp22ZH9qJgCnsgrwdNTSo8GVgNU3GxMpKtXh42SN96VHXnE5JzPzcbfX8ki08p5ZW2rYkZTF5Yo1Ps7WBLvZkVVQyvFz+QRdlXGfkVvMj7tTqefnRAN/59tfmLhZVnrWCdOs9NL8G2Slu0NYJ4juBRZ3dgFECHH/WFhYEB4ebu5hiAeEBM6FEEIIIYQQAORmFbF9aSIAVjYW1H3IF98IZ7PVUVapVLevtpJ1HDCAnYeSARrdU6l5fnbvpXrnH4NVNStlUVOkHVTew7JCsPeC5iPMPaKawy0MfBpC2gGIX6bUPgesLNSM6RxJVn4JbvZ3fzHK1c6Kke3DeLiOF4t2ppB0vgC1uScMPfwrFGSCrRsZwT1ZueIEAL0a+WKpUWNxzfiOnM2loKScQ2dyrusqzNPeJHA+uGUQHg5aQj3ssLW6EsYoLC1HfdUviQOnc9iXcpF9KReBU/i72FDf35n6fk6EedhhcWni4VuytFbKEV0uSWQwQF76laz0rBOXstLPK7XcT26AprHg1/SG5aHSc4qxsdLctJa9EEKI6ksC50IIIYQQQggAHN1s8I1wwVKrJqqVD5ZajbmHdHuXy7R41FG+qlTQehSsGq+UYNj5LbQdI/XO71Tin7DzOzDowCMK2r0J1o7mHlXNEtNXCZyf3KA8t3MzrrqXoPnVwj3tebd7NHtTLtI4wNm4PD4tFzsri6qrf55zBuJ/B8DQZAj/3X2Ocp2BGF9HejX0ve7im8Fg4OVO4aTlFJGeU0x6bjHncovRWmgI87Qn0tPepP3NMumvDqID1PdzoqSJH3Gnc0jMzOd0dhGns4tYFZeGjZWGNx6LIsTd7s6OTaUCRx/lEdpBWVZWDGf2KHe3FGTCpung21gJoDsodeuLy3QsP5jGmsPptAhx5fl2oXe2XyGEEGYngXMhhBBCCCFqqdzzRcRvTaPhwwFY2yvZkI0eDTBbhvldyTyqfL0cOAfQOsBDr8PaiUqNYs+1EPmYecZX0xgMcOB/cOQ35XVQW2j1kkyMeDc86yilbTLiIX4ZW5x6EuPriMsd1jW/HZVKRdMgF+Pr0nI9c7ckkV1YykPh7jzRxP/+ZjsbDLD7e6Wcj29jErUxHD57FAuNimdaBd3w94lKpSLK24Eob4dKHYq3kzU9GvjSo4Ev+SXlHD6TQ9ylx+WyMJdtSMggu7CU+n7OhLrb3VnGvqU1BLdVsswP/wJHV8DZfZAehyG6F3vs27NobwbZBaUA5JeUU67TVyzjXQghRLUhgXMhhBBCCCFqmfIyHcd2niPpwHkwGDi6PY1GnQMBalbQXFd+qeYwSlb01dwjoNHTsG8h7F0A7uHgKhmft1ReCtv/dWVCxHr9oH5/yda/FzF9IeNDio6uZVFJMOVWjvyzfwMcre9fILtUpyfc056dSRfYfPw8u5Kz6dHAh851vbC8NnBrMCjf7yNLldctRiplZu5E8hY4d1i5uNL0OcIdHBj7WCQZeSV4Olrffvv7xF5rQctQN1qGuqHXG0jPLcba8spdNBsSMkm9UMjyA2nYaS2o5+dIfT9n6vk54lDR74+ltfJ7JrQj7J5Lcep+zmz4D0Vlv+Ds0B21awOeahlEowDzlbwSQghx9yRwLoQQQgghRA2lK9OTevQCOZlFWFppsNRqsNAqXy21Gly8bbGyVj7yGwwGVCoV6Uk5HN50huL8MgC8w5yIauVtzsO4exdPga4ULG3Byf/69XW6K9m+Z3bDllnQ5SOwusMyDbVFcQ5s/KdSv1ltoQRQL5elEHfPuz64hZNx7ABN+Jui0EH3NWgOSsD4hQ5hPBLtyf92ppJ8voAle06z8Vgm/Zv50yTQBRVA6g6IWwI5qVc2XvMe1O2tTGiqqUC4oLRAuTgFykWCS5Ogxvg6EVPpR3b31GoVvs42xtcGg4HHYrw4eDqHQ2dyKCgpZ8fJC+w4eQGVCur7OfNa54iK78DRlwPhL7PqxDIeKluBk/4iz+t/xsP1NBqXYXLxSQghaigJnAshhBBCCFFDHfn7LCmHs266vk2/cKy8lY/8SfvPk7AjHb1OD4CNgxUx7f3wCq7BdauvLtNyo8CUSqWUGVl9qd75jm+VEi4SxDKVcxo2fAQF55ULC+3eBK+65h7Vg0GlIiukBxf376aBagdedUZW2a7DPR14r3s0205msWTPaTLzSvjqzxN80KwQ39QVcDFFaWhpA1HdIC8NTm1VJvk8s1eZK8Al+NY7ObBIueji4EO676NYFZTiWsmlaO4HlUpFmzB32oS5o9MbOJmZz8HTSkmX1AuF2FpdyUw3GAz8b2cqEV721PVxxE574zBKhLcD850asj+4KUMc9+KY/AecOwAr3oDoXhDTBywqp7a9EEKIqiEFtoQQQgghhKgh8i4UU5hbanwdVM8NW0crwpt6EtrYk4C6bviEO+Me4ICTpy1a2yuZrWUl5eh1elRqFWFNPenwVFTNDprDVRODRt68jdYe2r6uZFGn7oBjf1TN2GqKtIOwZoISNLf3gsemStC8kv12zotMC29ctQYCMjdU6b4vB4in9anH0IBzvK77Dt+4r5WguaUNurpPQK/Z0GAAtH1NubCkdVDu5vjjXTj0M+h1N+48KxGOrwXA0Hw4329L5b2lcRw8fbHqDrASaNQqIrwc6NfUn0m9Yvi0f0N6N/I1rj+dXcT6+HN8syGR1xbt56NV8aw4mMahMzn8uCsVg8EAKBOVTuxZl1ceq4djqyHQbTp4N1Bqvx/+Rcnmv9RWiPtJp9PRpk0b+vXrZ7I8JyeHgIAA3nvvPeOyn3/+mYcffhgXFxdsbW2Jiopi2LBh7Nu3z9hm/vz5qFQq48Pe3p6mTZvyyy+/VNkxAXTs2JExY8ZUuH1ycrLJuK2srAgPD2fq1KnGn1shbkcyzoUQQgghhKjGDAYDF84WcHJ/JhnJufjXcaXhIwEAOLrb0PGZOhWqnRve1IuAum5YWKmN5VtqNIPhSuDcPerWbd3DlTrEe/+jlJVwj7jzOs4PEoMBygrh1DbYPRcMOnCPhPZvgXUNv5hSzZzPL2Fb0gXO2XWktfVySFillBCqqpJBBgOc3oV13BLaXzyFwdYAFkqG+cXAR5myNoVHNXl0rmur1D8PbKXcwbFrDpzeDQd/hNN7lOzzq8sh6fWw6zvAAEFt2ZjjxcnMU1hbagh0ta2aY7tPrp281dpSw+Mx3hw8c5G0i8WcOJfPiXP5xvUBrra0DnMDwNn2qm0dfaHTPyB1p/K7J7ST3O0iqoRGo2HBggU0atSIH374gcGDBwPw6quv4urqyvvvvw/A+PHjmTFjBqNHj2by5Mn4+/uTkpLCli1b+Mc//sGqVauMfTo6OpKQoPzNzcvLY968eQwYMIDDhw8TFXWbv8Fmtm7dOmJiYigpKWHLli08//zz+Pj4MHz4cHMPTdQAknEuhBBCCCFENaTX6Tl7PJu/fzrB9qWJZCTngkqFrlxvkilV0QnnNJZqbB2tHoygOUB+BhRfVDLJ3cJv3z6qG/g3V7I/t8xUajM/aIpzlCDdsTVKpvCeBbDtX7DhEyXb9fcx8PPzsOhpWDJMCY4adBDUFh6eIEHz+2BVXBp6vQGrkNbYuQcpFyyOr7n/OzYYlMD36rdh8wwlg9xCiyqmL/SeDQ0HsvFUETmFZSzZc5oJSw+xLyVb2dbGWSnX0/plZf6AC4mwajzE/64EzAFOrIULJ8HSltyYp1iy5zQATzT2Mw0ePwA8HLQMaB7A1D71+eTJBjzTKogG/s44WFvQJMiFcE/7m2+sUkFgS+jxGUQ+XnWDFrVeREQEH330Ea+++ipnz57lt99+Y9GiRSxYsAArKyu2b9/OP//5Tz777DM+++wz2rVrR0hICB06dODdd99l5cqVJv2pVCq8vb3x9vYmIiKCqVOnolarOXjwoLFNdnY2Q4YMMWavd+3alePHj5v08/PPPxMTE4NWqyU4OJgZM2aYrP/qq6+IiIjA2toaLy8vnnzySQBiY2PZuHEjn3/+uTGDPDk5uULvhZubG97e3gQFBTF48GDatGnD3r17jet37drFo48+iru7O05OTnTo0MFkPcCkSZMIDAxEq9Xi6+vL6NGjjetKS0sZN24cfn5+2NnZ0bJlSzZs2FChsYnq7wH51CyEEEIIIcSDI+NULnEbz1Ccp5RlUVuoCajjQkhDD+ycpUYucKW+uUswWFQgUHe53vmqJCjIVALK7d4Ateb221ZX5aXK+5B+ENLjIDv5zra3tIE6PaBeP8mEvU9srCyw1Kjp0dAPip6AbbPh6AqI7AqW1pW/Q4MBzuyBuJ+unA8WWuXCUZ3uShmWS3o19MXdXsvPe5X657P/PEGLEFcGtwrCXmsBIe3Bq54yN0Daftj3X+XCTKPBcOB/SicNB/FjXB5FpToCXG3pVMez8o+pGnG319KpjuedH6fUNn/gGMr0N1+pApWFutLaqizvLuf11Vdf5ddff2XIkCHExcXx/vvv06hRIwD+97//YW9vz6hRo248rFv8TdDpdPznP/8BoEmTJsblsbGxHD9+nGXLluHo6Mj48ePp1q0bR44cwdLSkj179jBgwAAmTZrEwIED2bp1K6NGjcLNzY3Y2Fh2797N6NGjWbhwIW3atOHChQts3rwZgM8//5xjx45Rr149pkyZAoCHh8cdvye7d+9m7969DB061LgsLy+PoUOH8sUXXwAwY8YMunXrxvHjx3FwcGDJkiXMnDmTRYsWERMTQ3p6OgcOHDBu/9xzz5GcnMyiRYvw9fXl119/pUuXLsTFxRERcQeTDItqSQLnQgghhBBCVDNJB89TnFeKpbUFIQ3cCarnhpWNfHQ3cf6Y8tWjTsW3sbKDh8bC2veV4OK22dD6VVDXkBtxDQYlGHo5UJ55FHRlpm2cA5Va5Vb2SpDUyu7S1xu8rsgFB3FPnmzqT5d63kogWt9GCWjnn4PE9Uogu7IYDMqEnnE/QXaSssxCqwToo3uYBMwvU6lUtA13p2mQC8sPprH6UDo7ky6QkJ7H0DbBNAxwBltX6Pg2JP4JexcoP3frJioduIZx1KEl27YeR6WCIa2D0KjlAoyoHbJ+iL/pOit/exw7BxlfX1h8FEP5jWtqW3rb4tQl5Erbn49hKDadV8A9NuauxqhSqfj666+Jjo6mfv36vP3228Z1x44dIzQ0FAuLK58tPvvsM2MZF4AzZ87g5OQEKPXR7e2VuyuKioqwtLTk3//+N2FhStmzywHzv//+mzZt2gDwww8/EBAQwNKlS+nfvz+fffYZjzzyCBMmTAAgMjKSI0eOMH36dGJjY0lJScHOzo4ePXrg4OBAUFAQjRs3BsDJyQkrKytsbW3x9va+o/ehTZs2qNVqSktLKSsrY+TIkQwZMsS4/uGHHzZp/+233+Li4sLGjRvp0aMHKSkpeHt707lzZywtLQkMDKRFixYAJCYm8r///Y/Tp0/j66vMjfDmm2+yevVq5s2bx7Rp0+5orKL6kU/fQgghhBBCVDMteoRwLjkXd397LCxrcEb0/ZRxKWhxJ4FzUGqbPzRWKV9xaiuoNNBqVPUNnhecV4Lk6Qch/RCU5Jqut3FRJiD0rq88bJzNMkxxc/baS/92qzUQ00fJ4I7/HcIfvfeLFwYDnN0LcUuU0ilwKWDeRbmboALld6wtNTzZ1J8mgc58vyWJ9JxiDp/NVQLnoNyNEP6Icp7t+BrOHQZUlDd5jv9uTQWgQ5QnoR63KFkihDCLuXPnYmtrS1JSEqdPnyY4ONi47tqs8mHDhtGrVy927NjBM888Y1IWzsHBwVi+pLCwkHXr1vHCCy/g5uZGz549iY+Px8LCgpYtWxq3cXNzIyoqivh45e91fHw8vXv3Ntln27ZtmTVrFjqdjkcffZSgoCBCQ0Pp0qULXbp04YknnsDW9t7mTVi8eDHR0dGUlZURFxfH6NGjcXFx4eOPPwYgIyOD999/nz///JNz586h0+koLCwkJSUFgP79+zNr1izjuLp160bPnj2xsLBg7969GAwGIiNNJykvKSnBzc3tnsYtqgcJnAshhBBCCFHNqFQqvEOczD2M6qskD3LPKM89Im/d9kb8m8JDY5Ra58mbQaVWyrhUh3IlJflKYPJcnBIwz0s3XW+hBc8YJUju0wAc/arHuC/R6w2kZhcS5FZFk19WU3+fOI+Xo5Zwz2syvYPbK0Huwiw4uQEiH7u7HRgD5j8rNcjhjgPm1wr1sGdizxjWHEnn0bpexuXlOj0WGjXYeyi18FO2g6UNetcwGvqfoaj0Av2a+N3dcQhRQ7kNjr75ymt+JbsOvMUF3mvb9ruLv2k3sW3bNmbOnMmqVav45z//yfDhw1m3bh0qlYqIiAi2bNlCWVkZlpaWADg7O+Ps7Mzp06ev60utVhMefmU+kQYNGrBmzRo++eQTevbsaRJkv5rBYDAG6K9+fvX6yy4H5zds2MCaNWt4//33mTRpErt27cLZ2fmu34eAgADj2KOjozl58iQTJkxg0qRJWFtbExsbS2ZmJrNmzSIoKAitVkvr1q0pLS01bp+QkMDatWtZt24do0aNYvr06WzcuBG9Xo9Go2HPnj1oNKaJDpcz9EXNJoFzIYQQQgghqonzp/Nw8rTF0kqyzG/pcpkWBx+wvssLDAEtoM1o2PoFJG1UJhltMaLqg9CX65SfO6RklF84CVwdgFApWfKXs8rdI0FTff+N+/3gWQ6ezmFCj7rGZSvj0nCysSTAxRZfZ2slCPsAS71QyIKtyej0Bt7pVsc0eK6xgLq9YfdcOPIbhD18Z99PgwHO7ruUYX4pYK6xUgLm0T3u/ufhEisLNT0a+F61OwMz1h7D18ma/s0CsLbUQFBrpS3Qv1kAvRr5orWQ31midrmTuuP3q+2tFBUVMXToUF544QU6d+5MZGQk9erV49tvv+XFF1/kqaee4ssvv+Srr77itddeu6t9aDQaioqKAKhbty7l5eXs2LHDWKolKyuLY8eOER0dbWyzZcsWkz62bt1KZGSkMehsYWFB586d6dy5MxMnTsTZ2Zk///yTvn37YmVlhU5nWsbmbsddXl5OaWkp1tbWbN68ma+++opu3boBkJqayvnz5022sbGxoVevXvTq1YuXX36ZOnXqEBcXR+PGjdHpdGRkZNCuXbt7HpuofqrvJy4hhBBCCCFqkeKCMnatSEZjoeah/hHYOkr96ZvKTFC+3mmZlmsFtQaDHrZ+qdScVquh2fD7GzzX65Ua1JdLr2QeBX25aRtHvyulVzyjlbrkNUBRqY61R87hbGvJ2YtF+DrbUKbT8+u+M+j1ysUAtVqFr5M1Aa62+LvYEuFlT9gDVOKjXKfnu80n0ekNNA50vvGxhXaCQ79A4XnljoewTrfv2GBQJuiMWwJZJ5RlGiuIfByie95zwPxmjqbncezS49CZXIY9FEKkl3JMlzNHJWguRPXz9ttvo9fr+eSTTwAIDAxkxowZjB07li5dutC6dWveeOMN3njjDU6dOkXfvn0JCAggLS2N77//HpVKhfqqEmYGg4H0dOUOqKKiItauXcsff/xhrIkeERFB7969GTFiBN9++y0ODg68/fbb+Pn5GcuzvPHGGzRv3pwPPviAgQMHsm3bNmbPns1XX30FwPLlyzl58iTt27fHxcWFlStXotfriYqKAiA4OJgdO3aQnJyMvb09rq6uJmO8maysLNLT0ykvLycuLo7PP/+cTp064eio3JkTHh7OwoULadasGbm5ubz11lvY2NgYt58/fz46nY6WLVtia2vLwoULsbGxISgoCDc3NwYPHsyQIUOYMWMGjRs35vz58/z555/Ur1/fGIwXNZcEzoUQQgghhKgGTuzJQF+ux9HdBhsHS3MPp3rLPKp8vZsyLdcKbgsGHWz7Co6vVWqeN42tvOC5wQB5aUqQPP2gUoalrNC0jY3LlUC5Vz1lQsYaaOOxDIpKdTjZWOLjZA1AabmeTlGepGYXknqhkKJSHaezizidXQRk0SLElbAOSiDWYDCwdP8Z/JxtCXS1xdNBi7qGTTb5+8GznM4uwk5rwbOtg68rSwAodc2je8C+/8LhXyGkvVL//EZuGDC3hIjHoW6v+xYwvyzax5E3H49i7pYkzueXMP2PozTwdya/pJxnWwUR4HpvtYeFEJVv48aN/Otf/2LDhg3Y2V258DpixAiWLFliLNny6aef0qJFC77++mvmzp1LYWEhXl5etG/fnm3bthkDywC5ubn4+PgAoNVqCQoKYsqUKYwfP97YZt68ebz22mv06NGD0tJS2rdvz8qVK42lYJo0acKPP/7I+++/zwcffICPjw9TpkwhNjYWUErF/PLLL0yaNIni4mIiIiL43//+R0yMMjnqm2++ydChQ6lbty5FRUUkJSWZ1Gy/mc6dOwNKprmPjw/dunXjww8/NK6fO3cuI0eOpHHjxgQGBjJt2jTefPNN43pnZ2c+/vhjxo4di06no379+vz+++/GGubz5s1j6tSpvPHGG5w5cwY3Nzdat24tQfMHhATOhRBCCCGEMLPC3FJSDmcBENXS+8bBNqHQlUHWpRIV95pxfllIe9DrYMc3cGy1EsRs/OzdB88LL1wpvXLukFLP+mqWtuBZ90qw3NG3WtUpvxtlOj1rjpwDoGs9H+M5bKe14OmWgYASGM8qKCX1QiGp2UWkXigk2udKYCYjr4TlB9KMr60s1Pg52xDoZkuAiy1R3g74OttQXSWdL2DFQSUjc0jrIJxsbnEBLPxROLwU8s9ByjYIfsh0vcGgXGg5+OM1AfPHILpXlU4CG+3jyJTe9Vi8K4XNx89zIPUiALuSL0jgXIhqqEOHDpSXl99w3R9//GHyesCAAQwYMOCW/cXGxhqD27fi4uLCf/7zn1u26devH/369bvhuoceeogNGzbcdNvIyEi2bdt223FcFhwcfNPa61dr3Lgxu3btMln25JNPGp/36dOHPn363HR7S0tLJk+ezOTJkys8NlFzSOBcCCGEEELcN7oyPQU5JZQWl+PsZYuFpdzSfyPHd5/DoDfg5mePu/+DU7bivrhwUiltonVUapxXlrBOSub5zjlwdIWSed7o6YoFtEsLISP+Ukb5Ici5ZmI1tQV4RCnZ5N71wTX05hnGNdS2xCxyCstwtrWiVeiNM+ZVKhXu9lrc7bU0DnS5wXroEOVBSlYhp7OLKC3Xk3S+gKTzBQD0aOjDE439AcgtLmNDQiYBLjYEutriamdl1gtOpeV6vt9yEoPBQIsQV5oF3+auAUtrqNMdDi5WyrYEtVXeAINBmRQ27qcrtfw1lkqgvW4v5e4EM7Cx0hDbNoSmQa4s3J6MvdaS7g0q8edPCCGEqIYkcC6EEEIIIe5JeamOgpxSCnNKcHCzxt5FKdGQcSqXXcuTjO1snbQ0fjQQZy/JULxawcUSTh/NBiCqlbeZR1MDGOubR1Z+lnZ4Z6UG+e7vIX6ZEtxuMPD6/ejKlaDmuUvlV7ISlVrpRipwCb6UUV5PyYy30FbuWKsRvd7A6sNKpvVjMV53Pfmnp4M1Q1oHG/vMyCshNbuQlKxCUrMLCfe4Mslm8vkCftt3xvjaxkpDgKtS4iXAxZZoHwfc7KvuPd+RlEXaxWKcbCwZ3CqoYhtFPg7xv0PuGUjdCZY21S5gfq36/k7888mGGAwGuTNGCGFWL774Iv/9739vuO6ZZ57hm2++qeIRiQeRBM6FEEIIIcQdMegNpCVeJOXwBfKyiyktvHI7cFQrH8KbKoFzGwdlcktLrQZUKgpzStj6ywnqtPYhpKG7BF0uObbzHBgMeAY74uJdMyaBNKvKmhj0ZiIfUzLP98xX6k+rLaBeP8hOvlJ+JeMI6EpNt7P3uqpOeQxoHW7U+wNpX2o253KKsdVa0CHSo1L6VKtVeDtZ4+1kTfMbZG/bay1oHeZG6oVCzuYUU1SqM05iCTDsoRDahldd4PyhcHc0KhUO1pbYayv4b7aVHUR1hUM/w9YvrkwSq7aAiEeVkizVtN69/P4WQpjblClTTGqRX+3q+uxC3AsJnAshhBBCiAoruFjCzuVJFOaUmCy3srHA1kmLlc2V8hN2zloeHR6DlbUFpcXlxG04TXpiDvF/nyXrTD5NuwShvsvM1AeFwWBApQZUKiJbSLb5bRkMV00Mep8C56AEM/U62LdQyQBOWAmlBaZttI5KNrl3ffCqD/aVEzCuiQJcbHkowh13ey3WVVSOKdTDnlAPpaxRmU5P2sVi4wSkqdmFBLldubPlREYeBgNEeN2/ixkqlYo24e53vmFUVzi6HMpLlIB5eGeo27vaBsyFEKK68PT0xNPT09zDEA84CZwLIYQQQohbuvqWfBsHS/R6A5bWFoQ0cMcjyAE7J62SVX4NtVqFlbXycdPK2oImjwdx6lAW8X+nYW1nWeuD5qAE2xp1DiSqlTc29lbmHk71l3sWSvOVEhYuIfd3X9E9lAzgA/9TguYWWmVCz8t1yp0Da/yEnpXF09Ga59re5+/HLVhq1AS62RLodn0ZKIPBwH+3p5B6oZC6vo70buRHuGflzSOwNfE8Df2dsatolvm1tA7Q7k3IOg6hnSRgLoQQQlQjEjgXQgghhBA3VFpcTnLceTKS8mjTLwy1Ro1ao6ZZ12DsnK3ueKJPlUpFcH13XH3tsHO6UkKhrESHxlKNWl17g5ASNK+g85fKtLiFg6YK/pWJ6QPuEaBSg1tE1exTVKpSnZ5QDzvOXCziyNlcjpzNpZ6fE30a+xHifm+lkY6czeX7zUk421rxQZ8YbK3u8vzwaaA8hBBCCFGtyCc/IYQQQghhorigjJP7M0k5nIWuTJnwMD0pF99wZwCcPGzuqX9HtyvbG/QG9qxOxqA30OjRwFoVQE7cm4F3qBN2zg/upJGVLuNSmRb3qKrbp1dM1e2rhknJKmTVoTS61fchwLV6TvqrtdAwpHUwXev5sPzgWf4+kcWhMzkcOpNDA39nejXwuqt+C0vLmfe3Mvlxo0Dnuw+aCyGEEKLakvtjhRBCCCEEAAU5JcRtOM2f/4knaX8mujI9Du42NH4sCJ9Qp/uyz7zsYi6eK+LC2QI2Lz5OelLOfdlPdZN1Np+j29LYtPgYpcXlt99AKC5nnHtUYeBc3NTKQ2nsTLrAqkNp5h7KbXk4aHmubQjTnqhHm3B3VCo4ePoiGXklt9/4BhbvSuVCQSmejlr6N/Wv5NEKIYQQojqQy+JCCCGEEIKCiyVs+L8EZfJFwMXbjvBmnngEOhjrm98Pjm42tBsYwb4/UsjJLGTPymSCG7hTp7UPGosHM8fDYDBwbEc6AP5RLsY68OI2ii5CnvK+4R5p1qEIyMgtZnfyBQC61vMx82gqztPRmuEPhdC9vg9/nzhP4wAnVh1S1q2MS6OgpJy24e74Ot/8zpoDqRfZcvw8KhUMaxtSZROiCiGEEKJqPZj/jQghhBBCiFsyGAwUXLySaWnnrMXV1w6PIEda9QmjTb9wPIMc72vQ3LhvJy1t+oUR0sgDgOSD59m+NJHigrL7vm9zOJ+az4WzBag1asKbeZp7ODXH+WPKV6cA0Fbe5I7i7vxxOB2DAer5OVXbMi234u1kTb+m/sbfceU6PWuPnGP1oXQmLD3E1OVH+OtoBgUlpneE5JeUs2BrMgCP1fUmwsuhqocuhBBmFxwczKxZs+56+/nz5+Ps7Fxp47lX1W08dyI5ORmVSsX+/fvNPZQHkgTOhRBCCCFqEYPeQNqJi/y95ASbFh+jpOhKUKhFjxBa9AjBza/qg5JqjZq6bX1p3iMES62Gi+cK2bfmFIZLGfAPCoPBQMKlbPOgem61qqb7Pcu8VN9cyrSYXU5RGVtOnAegW/2ak21+KyqVimdaBdIowBmVSkXS+QL+u/0Ury/ez9cbEolPywXg131nyCkqw9vJmj6N/cw8aiGEuLHY2Fj69Olz3/rftWsXI0eOrFDbGwXZBw4cyLFjxyplLB07dkSlUl33ePHFFyulf1G7yX2hQgghhBC1gEEPqfHZnIq7QOGlTHO1hZqL5wrxCnYEqBalUTyDHGn7ZAT71qYQ086vSjLeq9K55FxyMgrRWKoJayrZ5nckU+qbVxfr489RrjMQ6mFHpNeDkf2vUatoGuRK0yBXcorK2HEyi79PnOd0dhG7ky/gYmtJtI8jTzT2o6RMx8N1PLGqBr8zhRDCHDw8PO5pexsbG2xs7m2y+auNGDGCKVOmmCyzta15d0OJ6kf+0gshhBBCPMDKS3Wc3H+e7DhrDm86S+HFEiy1GsKbefHwkGhj0Lw6sXPW0vbJcBzdr/xDlZNZVOOzz5Xa5ucACGngjtZGclgqrLwELiQpzz3qmHcstVxRqY4/j2YA0LW+zwN3cQvAycaSx2K8mdy7HhN7xtC5rhdtw90BsNda8Hy7UEI9HowLBkKI2mfjxo20aNECrVaLj48Pb7/9NuXlV+5AzMvLY/DgwdjZ2eHj48PMmTPp2LEjY8aMMba5Not80qRJBAYGotVq8fX1ZfTo0YCSDX7q1Clef/11YyY43Lg0yrJly2jWrBnW1ta4u7vTt2/fCh+Tra0t3t7eJg9HR+Uz7uVSJr/88gudOnXC1taWhg0bsm3btuv6+eOPP4iOjsbe3p4uXbqQlnZl8utdu3bx6KOP4u7ujpOTEx06dGDv3r0m26tUKr777jueeOIJbG1tiYiIYNmyZSZtDh8+TPfu3XF0dMTBwYF27dqRmJhoXD9v3jyio6OxtramTp06fPXVVybb79y5k8aNG2NtbU2zZs3Yt29fhd8nceckcC6EEEII8QArL9NzYlcG+jIV1naWRLf15eGh0US19K7Wgdurg3EXzhbw95Lj7FuTgq5Mb8ZR3RuD3oBPuBM2DlbGeu6igi6cBIMObFzATt47c1KpoHt9H+r4ONA4wNncw7nvAt1seapFYI2s4y6EqHwGg4GysrIqf1RW8sCZM2fo1q0bzZs358CBA3z99dd8//33TJ061dhm7Nix/P333yxbtoy1a9eyefPm6wLEV1uyZAkzZ87k22+/5fjx4yxdupT69esD8Msvv+Dv78+UKVNIS0szCURfbcWKFfTt25fu3buzb98+1q9fT7NmzSrlmC979913efPNN9m/fz+RkZE89dRTJhcMCgsL+fTTT1m4cCGbNm0iJSWFN99807g+Ly+PoUOHsnnzZrZv305ERATdunUjLy/PZD+TJ09mwIABHDx4kG7dujF48GAuXFAm0z5z5gzt27fH2tqaP//8kz179jBs2DDjOObMmcO7777Lhx9+SHx8PNOmTWPChAksWLAAgIKCAnr06EFUVBR79uxh0qRJJmMUla/6/rckhBBCCCHuWEFOCRnJuYQ0VIKL1naWhDX1IP9QKu2fikBrXfNqahfllQKQduIiBRdLaNY9uEbVBi8v02FhqUGtURPRzIuwJp6o1Q9elu79pDp/VZmWBzDDuSaxttTQtb4PXR+Q2uZCCHEnysvL+emnn6p8v/3798fS0vKe+/nqq68ICAhg9uzZqFQq6tSpw9mzZxk/fjzvv/8+BQUFLFiwgP/7v//jkUceAZQMaF9f35v2mZKSgre3N507d8bS0pLAwEBatGgBgKurKxqNBgcHB7y9vW/ax4cffsigQYOYPHmycVnDhg3v6Li+++47k2X/+te/GDp0qPH1m2++Sffu3QEluB0TE8OJEyeoU0e5k62srIxvvvmGsLAwAF555RWT8i8PP/ywSf/ffvstLi4ubNy4kR49ehiXx8bG8tRTTwEwbdo0vvzyS3bu3EmXLl3417/+hZOTE4sWLTJ+PyMjI43bfvDBB8yYMcOYbR8SEsKRI0f49ttvGTp0KD/88AM6nY65c+dia2tLTEwMp0+f5qWXXqrweyXujGScCyGEEEI8AC5mFLL3j1Ns+CGBI1vOkpNZZFwX1sQDa3cdak3NDDj6RbnQqncYVjYW5J4vYstPx8lOLzD3sG5Lr9Nzcl8m6xfEm3w/JGh+564EzqVMixBCCHG34uPjad26tcmdfW3btiU/P5/Tp09z8uRJysrKjIFvACcnJ6Kibj6/SP/+/SkqKiI0NJQRI0bw66+/mmRyV8T+/fuNgfq7MXjwYPbv32/yeOKJJ0zaNGjQwPjcx0e5+JuRkWFcZmtrawyaX25z9fqMjAxefPFFIiMjcXJywsnJifz8fFJSUm66Hzs7OxwcHIz97N+/n3bt2t3wIkhmZiapqakMHz4ce3t742Pq1KnGUi7x8fE0bNjQpH5769atK/5GiTsmGedCCCGEEDWUwWAg60w+iXszOZ965TZRjyDHBy4p19XXjrZPRrB7ZTJ5WUVsX3qS+p388Y9yMffQbigzNY/Dm89SkF0MQOqRLJw6+Jt5VDWUwYAq67jy3D3y1m3FfaPXG/h200maB7vQJNBFLgAJIWolCwsL+vfvb5b9VgaDwXDd3BSXy8CoVCqT5zdqcyMBAQEkJCSwdu1a1q1bx6hRo5g+fTobN26scJb8vU4U6uTkRHh4+C3bXD2Wy8en1+tvuP5ym6uPOzY2lszMTGbNmkVQUBBarZbWrVtTWlp60/1c7ufyfm51nJfbzJkzh5YtW5qs02g0wK2/D+L+kMC5EEIIIUQNVJhbyt7Vp8jJLFQWqFT4RToT2sjDZFLNB4mtoxVt+oWxf20K55JyObAuBa2tBR4BDoCS4a3WmPeGysLcUuK3niU9MQcAKxsL6rTxqbYB/prApvQ8lBaClQ24BJt7OLXWvtRsdidf4PDZHOr5OWGt1ph7SEIIUeVUKlWllEwxl7p16/Lzzz+bBNC3bt2Kg4MDfn5+ODs7Y2lpyc6dOwkICAAgNzeX48eP06FDh5v2a2NjQ69evejVqxcvv/wyderUIS4ujiZNmmBlZYVOp7vluBo0aMD69et57rnnKu9gK9nmzZv56quv6NatGwCpqamcP3/+jvpo0KABCxYsoKys7LrzyMvLCz8/P06ePMngwYNvuH3dunVZuHAhRUVFxiD89u3b7+JoREVJ4FwIIYQQogbS2llQXFiG2kJNYF1XQhp6YOtYc+p+3y0LSw1NuwZzbEc6BTmluPvbA0oGzqpvD6HRqLDQarC85uHsZUtwfXdjPxmncrGw1GBpfaWNWqO6LsPqTpzcn0nCjnT05XpQqQiu70ZkC28stRJgvBcOxWeVApNuESDBWrMwGAysjEsH4OE6nlhbyvdBCCGqu5ycHPbv32+ybOTIkcyaNYtXX32VV155hYSEBCZOnMjYsWNRq9U4ODgwdOhQ3nrrLVxdXfH09GTixImo1eqbfkaaP38+Op2Oli1bYmtry8KFC7GxsSEoKAiA4OBgNm3axKBBg9Bqtbi7u1/Xx8SJE3nkkUcICwtj0KBBlJeXs2rVKsaNG1ehYy0sLCQ9Pd1kmVarxcWl8hIXwsPDWbhwIc2aNSM3N5e33nrrjjPlX3nlFb788ksGDRrEO++8g5OTE9u3b6dFixZERUUxadIkRo8ejaOjI127dqWkpITdu3eTnZ3N2LFjefrpp3n33XcZPnw47733HsnJyXz66aeVdoziehI4F0IIIYSo5spKdKQczuJcci6t+4ShUqvQaNQ0eTwIO2ctWpva9ZFOpVIR1crHJFuqvFQPBgO6cgO6cj0lBWUm2+jK9cbAucFgYNeKZLjmdleVWoWltQbPQEcaPhJgXB6/9Sxqjfq6YLylVoOVjQXWdkrGkFqjQl+ux9XXnpj2vji6PZiZ/1XNofgs2CL1zc1oX+pFks8XYKlR07mul7mHI4QQogI2bNhA48aNTZYNHTqUlStX8tZbb9GwYUNcXV2NQdjLPvvsM1588UV69OiBo6Mj48aNIzU1FWtr6xvux9nZmY8//pixY8ei0+moX78+v//+O25ubgBMmTKFF154gbCwMEpKSm5YbqRjx4789NNPfPDBB3z88cc4OjrSvn37Ch/rnDlzmDNnjsmyxx9/nNWrV1e4j9uZO3cuI0eOpHHjxgQGBjJt2jTefPPNO+rDzc2NP//8k7feeosOHTqg0Who1KgRbdu2BeD555/H1taW6dOnM27cOOzs7Khfvz5jxowBwN7ent9//50XX3yRxo0bU7duXT755BP69etXaccpTKkMUiCn0uTm5uLk5EROTg6Ojo7mHs59V1ZWxsqVK+nWrVuNvlVJVF9yjonaQM5zcSvFBWUkHzzPqUNZlJcqt7g26RKMT5jTHfVTG84zg8FAWYnuukf5pa+2Tlrj+6Yr07NtaaJJu6uD6N5hTjTtEmzsd+XXcdcF2S9z87enVW9lIimD3kDGqTw8gx3uKXNdXFFWVsbZr/sS4GqN+pEJ4NPg9huJSrX5eCb/2XYKvd7AI9FePN0y0NxDqlS14fejEHDv53pti3cAFBcXk5SUREhIyE2DxrVBQUEBfn5+zJgxg+HDh5t7OELcszv52a5d6UlCCCGEEDVAQU4JJ/dlcvpoNnqdMlGQvYs1oU088Ap2MPPoqieVSoWVtQVW1rf/eKuxVPNQ/wjja4PBQHmZ3hhkv7pOukFvILyJB2UlOkqvCsRffmhtrppoSq3CK6R2BBOqTGEW2vJcUNmAe8Tt299H5To9FmauoV/Vlu47w+8HzgLQIsSVJ5vKBLdCCPGg27dvH0ePHqVFixbk5OQwZcoUAHr37m3mkQlR9SRwLoQQQghRjeRnF7Pxf8eMGc7O3naENfHAK9hRspjvE5VKhaWVBksrDTbXXJdQa9REtfIxz8AEqvPHADA4B4GleUvffPHnCUrKdDQNcqFZsCuudg/+nAL+LjaoVNCjgS+9G/nK7yAhhKglPv30UxISErCysqJp06Zs3rz5hrXJ77fNmzfTtWvXm67Pz8+vwtGI2kgC50IIIYQQ1YidsxYXL1sstBrCmnjg6mMnwSpRe10KnOMeWaW71ekN/N+OU7SP9CDIzY6iUh1H03LR6Q2cyMhn8a5UQj3saBrkSrNgF9zttVU6vvvp6rkDmgW7MtnZBj9nqdcvhBC1RePGjdmzZ4+5hwFAs2bNrpvgVIiqJIFzIYQQQggzKi0uJ2F7OnVa+2Cp1aBSqWjZOxSNRe0qCSHEjajOJwBgcI+qsn3q9Qa+33KSHScvsDflIh/1rY+NlYaP+zVg76lsdp/K5kRGHiczCziZWcBPu1PpWMeTZ1sFVdkY75fUC4X8Z1syL3UMN2bUS9BcCCGEudjY2BAeHm7uYYhaTALnQgghhBBmcjGjkL2rT1GUV0pZiY4mjyuBNwmaCwGc2obq4ikADFWUca7XG5j7dxI7Tl5ArVbxbOsgrC01ALjaWdG5rhed63pxsbCUvSnZ7E7O5ti5PPxdrgSXcwrL2Hwik2ZBrng71ZzJ5A6kXuTbTYmUlOlZvCuVlzqGmXtIQgghhBBmJYFzIYQQQogqZjAYSDlygcObzmDQG7B10hLWxNPcwxKi+khYBXsWgMHAeYdo3Gzd7vsuLwfNtyVmoVareLFDGE0CXW7Y1tnWiofrePFwHS9yisqwumrS0L0p2fy69wy/7j2Dv4sNTYNdaRbkgm81zdw2GAysj89g0a4UDAaI9nFkSOuanz0vhBBCCHGvJHAuhBBCCFGFdGV6Dm06w+mjFwDwCnGk4SOBWGo1Zh6ZENWAwQAH/gdHflNehj9KYroH97tQi8FgYN7WZLYlZqFSqXixQyhNg24cNL+Wk42lyWt3ey0xfk7Ep+VyOruI09ln+G3fGXycrWkW5Ernul7Ya6vHv2E6vYH/25nChqMZALSP9GBwy0AsNHLXixBCCCFE9fjEJoQQQghRCxTmlrJ7ZTJ5WUWgUhHV0puwJh4y+acQALpy2PktJG1SXjcchD6iO6xadd93vebIObaeOI9KpeKFDqE0DXK9677q+ztR39+J/JJyDqReZFfyBY6czSXtYjErctN4tK6XsW1+STl2Vpr7+jvAYDBQXKanoLSc4jId/i62xuX/3nSS3ckXUKngyaYBPB7jJb+PhBBCCCEuMWsqwaZNm+jZsye+vr6oVCqWLl1qst5gMDBp0iR8fX2xsbGhY8eOHD582KRNeno6zz77LN7e3tjZ2dGkSROWLFli0iY7O5tnn30WJycnnJycePbZZ7l48aJJm5SUFHr27ImdnR3u7u6MHj2a0tLS+3HYQgghhKilLCzVlJWUY2VjQcteoYQ39ZQglRAAZcWw6Z9K0FylhlYvQcwTUEU/Hx2jPIjxdWRk+1CaB9990Pxq9loL2oa7M6ZzJLMGNWJ4uxB6NPDB7qps88/XHeMfv8axZM9pks8XYDAY7nm/329J4qOV8bz7axxjFu1j5MI9vPJ/exm/5CAfrzpqbKdSqSgqLcfKQs2oTuF0qectv4+EEEIIIa5i1sB5QUEBDRs2ZPbs2Tdc/89//pPPPvuM2bNns2vXLry9vXn00UfJy8sztnn22WdJSEhg2bJlxMXF0bdvXwYOHMi+ffuMbZ5++mn279/P6tWrWb16Nfv37+fZZ581rtfpdHTv3p2CggK2bNnCokWL+Pnnn3njjTfu38ELIYQQola4OhBmZWNB8+4htBsYibu/vRlHJUQ1UpwD66dA2gHQWEH7tyC0433f7dU/m1oLDa8/GkmLkMoJml/L1sqCNmHu9G7kZ1xWUFJO6oUiMnJLWBWXxgfLj/D2z3H8uDuVk5n5tw2iZxeUsvXEeX7clWqyPL+4nBMZ+aTnFJNXXI5er/RjqVFjbakxvgZoEeLGP7pF37SWuxBCCHErwcHBzJo1q9Lb1mbJycmoVCr2799v7qHclQft+2zWUi1du3ala9euN1xnMBiYNWsW7777Ln379gVgwYIFeHl58X//93+88MILAGzbto2vv/6aFi1aAPDee+8xc+ZM9u7dS+PGjYmPj2f16tVs376dli1bAjBnzhxat25NQkICUVFRrFmzhiNHjpCamoqvry8AM2bMIDY2lg8//BBHR8f7/VYIIYQQ4gFUXqpj98pk/KJcCIhWAnKO7tVzgkAhzCLvHPz1IeSfAyt76Pg2uEfc990aDAb+u/0UDtaW9G6k3P1a1dnWdloLZg1qRNyZHHYnZ3Pw9EXO55fwx6F0/jiUzkMR7jzXNsTYvrhMx9H0PI6czeVIWg5pF4uN6x6J9sTNXgtAr0a+PBThhp3WAjsrC+y1FthpLbCyuD5n6qEI9/t/oEIIIapcbGwsCxYsAMDCwgJXV1caNGjAU089RWxsLGp15eTR7tq1Czs7u0pvWxG3+7s9dOhQ5s+fX2n7q4hJkyYxefLk65ZHRUVx9OjRG2whqrtqW+M8KSmJ9PR0HnvsMeMyrVZLhw4d2Lp1qzFw/tBDD7F48WK6d++Os7MzP/74IyUlJXTs2BFQAutOTk7GoDlAq1atcHJyYuvWrURFRbFt2zbq1atnDJoDPP7445SUlLBnzx46depUNQcthBBCiAeGTqdn96pkss7kk3ehGJ9wJywsZQJQIYwunIS/PoKSXLBzh07vgqPv7be7RwaDgR92pLAhIROVCpoEuhDoZnvf93sj1pYamge70jzYleIyHYfPKkH0A6cvEu1zJXknJauQD1YcMckWV6kgyM2OaB9H1FcFD0Lc7Qhxr7zAhBBCiJqpS5cuzJs3D51Ox7lz51i9ejWvvfYaS5YsYdmyZVhY3HtI0MPD4760rYi0tDTj88WLF/P++++TkJBgXGZjY5qsUlZWhqWl6YTe90NMTAzr1q0zWVYZ77Uwj2r7nUtPTwfAy8vLZLmXlxenTp0yvl68eDEDBw7Ezc0NCwsLbG1t+fXXXwkLCzP24+npeV3/np6exn2kp6dftx8XFxesrKyMbW6kpKSEkpIS4+vc3FxA+WEsKyu7k8OtkS4fY204VmEeco6J2kDO8weTQW9g/7rTZKbmobFQ0+hxfwzoKSvTm2U8cp6Jaic9Ds3fM6G8GINLMPp248DGGa45R+/HuftXQibr49NRoWJIq0B8HC2rxc+GBmjg60ADXwdKy/1Rqa4c9/6ULMp1OjwdtNTxdiDa24Eobwfsr6qXXh2OoSaS34+itrjXc11+RmoerVaLt7c3AH5+fjRp0oRWrVrxyCOPMH/+fJ5//nlycnJ46623WLp0KcXFxTRr1oyZM2fSsGFDYz/Lli1jypQpHDp0CHt7e9q3b88vv/wCKGU5xowZw5gxYwAl43ru3LmcO3cONzc3nnzySb744osbtk1JSeHVV19l/fr1qNVqunTpwpdffmmMz02aNImlS5fyxhtvMGHCBLKzs+natStz5szBwcHBeGwATk5OqFQq47Lk5GR8fHxYvHgxX331Fdu3b+frr7/mueeeY968efzzn/8kKSmJ4OBgRo8ezahRo4x9nTlzhrFjx7JmzRrUajUPPfQQn3/+OcHBwRV63y0sLEzGdq3g4GBGjhzJiRMn+Omnn3BxceG9995j5MiRJu1OnjzJ66+/zo4dO4iIiOCbb76hdevWAGRlZfHKK6+wefNmLly4QFhYGP/4xz946qmnjNt37NiRBg0aYG1tzXfffYeVlRUvvvgikyZNMra5ePEi48aN47fffiMnJ4fw8HA+/vhjevToAcDWrVt5++232bVrF+7u7jzxxBN89NFHxjsHMjIyGD58OOvWrcPb25upU6dW6D2qSapt4Pyya2+9MBgMJsvee+89srOzWbduHe7u7ixdupT+/fuzefNm6tevf8M+btRPRdpc66OPPrrhLRhr1qzB1tY8WSvmsHbtWnMPQTzg5BwTtYGc5w8OgwEKTllSfN4C1AYcw0vZujvJ3MMC5DwT1YDBgEfeYULOr0Nl0JNjE8hxu+bo/tp6y80q69zNKIJlp9TogZYeBi4mpLEy4babmV1WMTxsD46WQBacy4Jzh809qgeL/H4UtcXdnuuFhYWVPJKayWAwoNeXVvl+1WqrSikp9vDDD9OwYUN++eUXhg8fTvfu3XF1dWXlypU4OTnx7bff8sgjj3Ds2DFcXV1ZsWIFffv25d1332XhwoWUlpayYsWKG/a9ZMkSZs6cyaJFi4iJiSE9PZ0DBw7csK3BYKBPnz7Y2dmxceNGysvLGTVqFAMHDmTDhg3GdomJiSxdupTly5eTnZ3NgAED+Pjjj/nwww8rdLzjx49nxowZzJs3D61Wy5w5c5g4cSKzZ8+mcePG7Nu3jxEjRmBnZ8fQoUMpLCykU6dOtGvXjk2bNmFhYcHUqVPp0qULBw8exMrK6o7f8xuZMWMGH3zwAf/4xz9YsmQJL730Eu3bt6dOnTrGNu+++y6ffvopERERvPvuuzz11FOcOHECCwsLiouLadq0KePHj8fR0ZEVK1bw7LPPEhoaalJxY8GCBYwdO5YdO3awbds2YmNjadu2LY8++ih6vZ6uXbuSl5fHf//7X8LCwjhy5AgajXKHbFxcHI8//jgffPAB33//PZmZmbzyyiu88sorzJs3D1BKAqWmpvLnn39iZWXF6NGjycjIqJT3qLqotoHzy1dn0tPT8fHxMS7PyMgwXn1KTExk9uzZHDp0iJiYGAAaNmzI5s2b+de//sU333yDt7c3586du67/zMxMYz/e3t7s2LHDZH12djZlZWXXZaJf7Z133mHs2LHG17m5uQQEBPDYY4/VirroZWVlrF27lkcffbRKbncRtY+cY6I2kPP8wXNsxzlOnj6Poxc0ejQA71DzfyaQ80xUC5lHUR/4P1T5J8DDHUNga9xavkSo+ub/klTmuZtfUs7UlUdx9yylSYAzL7QPqfK65qL6kd+Pora413P98h32tZ1eX8qhQ69U+X7r1ZuNRqOtlL7q1KnDwYMH+euvv4iLiyMjIwOtVun7008/ZenSpSxZsoSRI0fy4YcfMmjQIJOk0auz0a+WkpKCt7c3nTt3xtLSksDAQON8hNdat24dBw8eJCkpiYCAAAAWLlxITEwMu3btonnz5gDo9Xrmz5+Pg4MDAM8++yzr16+vcOB8zJgxxnkTAT744ANmzJhhXBYSEsKRI0f49ttvGTp0KIsWLUKtVvPdd98ZPyPMmzcPZ2dnNmzYYFJO+mbi4uKwt7c3WTZo0CC+++474+tu3boZs9zHjx/PzJkz2bBhg0ng/M0336R79+4ATJ48mZiYGE6cOEGdOnXw8/PjzTffNLZ99dVXWb16NT/99JNJ4LxBgwZMnDgRgIiICGbPns369et59NFHWbduHTt37iQ+Pp7IyEgAQkNDjdtOnz6dp59+2niHQEREBF988QUdOnTg66+/JiUlhVWrVpnMKfn9998THR192/eoJqm2gfOQkBC8vb1Zu3YtjRs3BqC0tJSNGzfyySefAFeueF47qYFGo0GvV26Fbt26NTk5OezcudP4A7tjxw5ycnJo06aNsc2HH35IWlqaMUi/Zs0atFotTZs2vekYtVqt8ZfL1SwtLWvVh67adryi6sk5JmoDOc8fDBmnckk+kIVapaJ+J38CotzMPSQTcp4Js8g5Dfv/D87sUV5bWkNMH4jpi6aCget7PXcNBgMLNiZxsbAcbycbhrcPw8qq2v4rJMxAfj+K2uJuz3X5+XhwXK6usGfPHvLz83FzM/28WlRURGJiIgD79+9nxIgRFeq3f//+zJo1i9DQULp06UK3bt3o2bPnDet7x8fHExAQYAyaA9StWxdnZ2fi4+ONgfPg4GBj0BzAx8fnjjKamzVrZnyemZlJamoqw4cPNzmm8vJynJycANizZw8nTpww2SdAcXGx8T25naioKJYtW2ay7Nr+GjRoYHx+ucTMtcd1dZvLscqMjAzq1KmDTqfj448/ZvHixZw5c8ZYSvrayVev7uNyP5f3s3//fvz9/Y1B82tdfi9++OEH4zLljgs9SUlJHDt2DAsLC5P3uE6dOjg7O9+wv5rKrJ8W8/PzOXHihPF1UlIS+/fvx9XVlcDAQMaMGcO0adOIiIggIiKCadOmYWtry9NPPw0o35Dw8HBeeOEFPv30U9zc3Fi6dClr165l+fLlAERHR9OlSxdGjBjBt99+C8DIkSPp0aMHUVFRADz22GPUrVuXZ599lunTp3PhwgXefPNNRowYUSsyx4UQQghROTwCHQhp5IGVtQWBdatX0FyIKld4AQ7+CCc3AAZQqSHsYajXD2xdq3w4TQJdSMwsYFTHcGwlaC6EEOIuqNVW1Ks32yz7rSzx8fGEhISg1+vx8fExKY1y2eXg57UTbN5KQEAACQkJrF27lnXr1jFq1CimT5/Oxo0br7vwcrPSyNcuv3Y7lUplTJStiKsDyZe3mzNnjklWNmAsT6LX62natKlJsPiyik5uamVlRXh4+C3bVOS4rm5z+T253GbGjBnMnDmTWbNmUb9+fezs7BgzZgylpaU37ePa/dzue6vX63nhhRcYPXr0desCAwONE7E+6HfvmfUT4+7du+nUqZPx9eWyJ0OHDmX+/PmMGzeOoqIiRo0aRXZ2Ni1btmTNmjXGKzWWlpasXLmSt99+m549e5Kfn094eDgLFiygW7duxn5/+OEHRo8ebbylolevXsyefeUXnUajYcWKFYwaNYq2bdtiY2PD008/zaeffloVb4MQQgghHhAqlYroNj4P/AdIIW6ptACOLIOEFaC7NJlcQAto+BQ4+pplSCqVivaRHjQPdsXGSmOWMQghhKj5VCpVpZVMMYc///yTuLg4Xn/9dfz9/UlPT8fCwuKmE182aNCA9evX89xzz1WofxsbG3r16kWvXr14+eWXqVOnDnFxcTRp0sSkXd26dUlJSSE1NdWYdX7kyBFycnLuW6kPLy8v/Pz8OHnyJIMHD75hmyZNmrB48WI8PT2rdSLt5s2b6d27N8888wygBLmPHz9+R+9dgwYNOH36NMeOHbth1nmTJk04fPjwTS8CREdHU15ezu7du40VPhISErh48eKdH1A1ZtbAeceOHTEYDDddr1KpmDRpksmMr9eKiIjg559/vuV+XF1d+e9//3vLNoGBgcYsdSGEEEKIijp/Op+UI1k0fDgAjYVaguai9tKVwfE1cOgXKM1XlnlEQaNnwOPGtwHfbzmFZWg0Kuy1yr89EjQXQghRW5SUlJCeno5Op+PcuXOsXr2ajz76iB49ejBkyBDUajWtW7emT58+fPLJJ0RFRXH27FlWrlxJnz59aNasGRMnTuSRRx4hLCyMQYMGUV5ezqpVqxg3btx1+5s/fz46nY6WLVtia2vLwoULsbGxISgo6Lq2nTt3pkGDBgwePJhZs2YZJwft0KGDSemPyjZp0iRGjx6No6MjXbt2paSkhN27d5Odnc3YsWMZPHgw06dPp3fv3kyZMgV/f39SUlL45ZdfeOutt/D397/tPsrLy0lPTzdZplKpbjmH4p0KDw/n559/ZuvWrbi4uPDZZ5+Rnp5+R4HzDh060L59e/r168dnn31GeHg4R48eRaVS0aVLF8aPH0+rVq14+eWXjROoxsfHs3btWr788kuioqKMFT7+/e9/Y2FhwZgxY+7oLoWaQH37JkIIIYQQ4kYunitk98ok0o5f5OS+THMPRwjzMBggeQssHwN7/6MEzR39oP1b0Hmy2YLmOr2BrzcmMuX3w6RkFZplDEIIIYS5rF69Gh8fH4KDg+nSpQt//fUXX3zxBb/99hsajQaVSsXKlStp3749w4YNIzIykkGDBpGcnGwM8nbs2JGffvqJZcuW0ahRIx5++GF27Nhxw/05OzszZ84c2rZta8xU//3336+roQ5KIHnp0qW4uLjQvn17OnfuTGhoKIsXL76v78nzzz/Pd999x/z586lfvz4dOnRg/vz5hISEAGBra8umTZsIDAykb9++REdHM2zYMIqKiiqcgX748GF8fHxMHje6eHAvJkyYQJMmTXj88cfp2LEj3t7e9OnT5477+fnnn2nevDlPPfUUdevWZdy4ceh0OkDJSN+4cSPHjx+nXbt2NG7cmAkTJhjrrYMycWpAQAAdOnSgb9++jBw5Ek9Pz8o6zGpBZbhVyre4I7m5uTg5OZGTk1Otb+moLGVlZaxcuZJu3brJRCHivpBzTNQGcp7XXPnZxWz9JZGy4nLc/Oxp3iMEjUX1zEmQ80zcN2kHYf8PkJ2svLZxgfr9IbQjqO89u/tezt2fdqey+lA61pYa3u9ZFy9H63sej3jwyO9HUVvc67le2+IdoEwImZSUREhICNbW8jdEiAfFnfxsy6w4QgghhBB3qCi/lB3LTlJWXI6Thy1NuwVX26C5EPfFhSQlYJ4ep7y2tIHoXlCnO1iYv/brvpRsVh9SbpMe9lCwBM2FEEIIIcQdk8C5EEIIIUQFGQwG4remcfb4RUoKyrBz1tK8ZwiWUjdZ1Bb5mXBwkVKaBUBtARGPQkxfsK4eGYgZecV8vyUJgEfretE0yNXMIxJCCCHEg8De3v6m61atWkW7du2qcDSiKkjgXAghhBDiBgwGAwUXS8nNKsI33BlQ6jFeSCugpKAMGwcrWvYKRWsjH6fEfVCcq5Q/8aoH6mpwN0NJnjLp5/E1oC9XlgW1gQaDwKHyJru6V6Xler7ekEhRqY5QDzuebHr7SbyEEEIIISpi//79N13n5+dXdQMRVUb+0xNCCCGEuKS8TEfWmQIyT+WSmZJHYW4pKrUKjwAHLLVKVnl4U08MevAItMfCUjLNxX2gK4f1kyHnNLiFQ8sXwTnAPGMpL4WElXDkNyi7NMGmVz1o9DS4hZlnTLew/OBZUrIKsdNa8FLHcCw01eCigxBCCCEeCOHh4eYegqhiEjgXQgghRK1nMBg4tPEMqfEXMOivzJuuUqtw9bGjtLjcGDj3DnEy1zBFbXF8jRI0B8g6Aavfhrq9IeYJ0FTR5IV6PSRtgIM/QdEFZZlzEDQeDN4NQKWqmnHcocdivDmdXcTDdTxxtbMy93CEEEIIIUQNJoFzIYQQQtR6Gcl5pBzOAsDGwQrPIAfcAx1w95esclHFinMg7iflef3+cOEknNkDh36GlO3Q8gXwiLp/+zcY4MxeZeLP3DPKMjt3aDAQgttV24D5ZfZaC0Y/EmHuYQghhBBCiAeABM6FEEIIUes5elgT0tADjaWayBZeqKp5cFA8wA4sVkqiuAQrE26qVErAfPdcJZC9diJEPgYNnwJLm8rd9/njsO+/kHlUeW1lr2S5RzwGFpK9LYQQQgghahcJnAshhBCi1rOxt6LuQ77mHoao7bISIfFP5XmzYVcmBQ1qDd71YO9CSNoIx/6A03ugxfPg2/je95t7Fg78D1J3Kq81lhDVTSkPY2V37/1XgRUH0ziSlsPDdTxpGuRq7uEIIYQQQogHgATOhRBCCCGEMDeDAfbMBwwQ1Pb6cixaB2g9CoIfgp3fQsF52PCx8rrJELC+i9r7RRfh0BI4sR4MekAFoR2g/gCwc7v3Y6pCR9JyOJqWR/NgCZoLIYQQQojKIdPMCyGEEKLWyrtQzK4VSVw4W2DuoYjaLnkLnD8GFlpo/MzN2/k0gG4zoE53QKVst+INOLYG0g8pNdHzM6AkXwnG30hZkTLp5++j4fhaJWju2wS6/RNavVTjguY6vYGk88rPcLinvZlHI4QQQtRuwcHBzJo1y9zDqFVq8nseGxtLnz59zD2Mm5KMcyGEEELUWif3ZZKRnItKrcLVt2aUpBAPoLJiZTJOgLp9wPY2WdOW1kqWeWAbJfv8Ygrs/v4GDVVKHXQrW6VeuZUdWNpCZgKU5CpN3MKh0WDwqluZR1SlzmQXUVKmx9pKg59zJdd9F0IIIWqg2NhYFixYAIBGo8HX15fu3bszbdo0XFxczDy6yhEcHMypU6dMlvn5+XH69GkzjUgZ05gxYxgzZkyF2m/YsIFOnTrdcF1aWhre3t6VODpxNyRwLoQQQohaqSi/lDPHsgEIa+xh5tGIWu3wr1CUDfaeUKdHxbdzD4fHP4Kjy+HsXigtuPLQlQIGZaLRskKltMvVHLyVCUYDWioTkNZgiZn5AIR52MvEvkIIIcQlXbp0Yd68eZSXl3PkyBGGDRvGxYsX+d///mfuoVWaKVOmMGLECONrjUZz132VlZVhaWlZGcO6YwkJCTg6Opos8/T0NMtYhCkp1SKEEEKIWinpwHkMegOuvva4eEu2uTCTvHQl8A3QZChYWN3Z9hoLiOkDj06B7jPgiW9g4EIY+AP0/Tf0mAmPTYWOb0Ob0dBsuPK12wwIbFXjg+YAJzIuB87l51gIIYS4TKvV4u3tjb+/P4899hgDBw5kzZo1AOh0OoYPH05ISAg2NjZERUXx+eefm2x/uYTGp59+io+PD25ubrz88suUlZUZ22RkZNCzZ09sbGwICQnhhx9+uG4cKSkp9O7dG3t7exwdHRkwYADnzp0zrp80aRKNGjVi7ty5BAYGYm9vz0svvYROp+Of//wn3t7eeHp68uGHH17Xt4ODA97e3saHh8eVZJivv/6asLAwrKysiIqKYuHChSbbqlQqvvnmG3r37o2dnR1Tp04F4Pfff6dp06ZYW1sTGhrK5MmTKS8vNxlvYGAgWq0WX19fRo8eDUDHjh05deoUr7/+OiqV6o4u5nt6epoch7e3N+pLk8RX5PsAUFhYyLBhw3BwcCAwMJB///vfJuvHjx9PZGQktra2hIaGMmHCBJM+Ln8fFi5cSHBwME5OTgwaNIi8vDxjG71ezyeffEJ4eDharZbAwECT78uZM2cYOHAgLi4uuLm50bt3b5KTk43rdTodY8eOxdnZGTc3N8aNG4fhZqUFqwnJOBdCCCFErVNWoiPlcBYAYU0k21yY0d6FoC8H7wbg17Ty+tVYgMbp7iYNrWEuB86lvrkQQoj7zWAwUKKv+kCfVn1ngdhrnTx5ktWrVxszqvV6Pf7+/vz444+4u7uzdetWRo4ciY+PDwMGDDBu99dff+Hj48Nff/3FiRMnGDhwII0aNTJmecfGxpKamsqff/6JlZUVo0ePJiMjw7i9wWCgT58+2NnZsXHjRsrLyxk1ahQDBw5kw4YNxnaJiYmsWrWK1atXk5iYyJNPPklSUhKRkZFs3LiRrVu3MmzYMB555BFatWp12+P99ddfee2115g1axadO3dm+fLlPPfcc/j7+5uURpk4cSIfffQRM2fORKPR8Mcff/DMM8/wxRdf0K5dOxITExk5cqSx7ZIlS5g5cyaLFi0iJiaG9PR0Dhw4AMAvv/xCw4YNGTlypEkWfGW43fcBYMaMGXzwwQf84x//YMmSJbz00ku0b9+eOnXqAMpFhvnz5+Pr60tcXBwjRozAwcGBcePGGftITExk6dKlLF++nOzsbAYMGMDHH39sDI6/8847zJkzh5kzZ/LQQw+RlpbG0aNHASVw36lTJ9q1a8emTZuwsLBg6tSpdOnShYMHD2JlZcWMGTOYO3cu33//PXXr1mXGjBn8+uuvPPzww5X6flUmCZwLIYQQotY5dSgLXZkeB1drPAIdzD0cUVud3Q9ndoNKA02HPhDZ31WtTKfH28maojIdoe4SOBdCCHF/legNjDicXOX7nRMTjLXmzj4nLF++HHt7e3Q6HcXFxQB89tlnAFhaWjJ58mRj25CQELZu3cqPP/5oEjh3cXFh9uzZaDQa6tSpQ/fu3Vm/fj0jRozg2LFjrFq1iu3bt9OyZUsAvv/+e6Kjo43br1u3joMHD5KUlERAQAAACxcuJCYmhl27dtG8eXNACeTPnTsXBwcH6tatS6dOnUhISGDlypWo1WqioqL45JNP2LBhg0ngfPz48bz33nvG19OmTWP06NF8+umnxMbGMmrUKADGjh3L9u3b+fTTT00C508//TTDhg0zvn722Wd5++23GTp0KAChoaF88MEHjBs3jokTJ5KSkoK3tzedO3fG0tKSwMBAWrRoAYCrqysajcaYBX8n/P39TV77+fmRkJBQoe/DZd26dTMe7/jx45k5cyYbNmwwBs6vfp+Cg4N54403WLx4sUngXK/XM3/+fBwcHIzvx/r16/nwww/Jy8vj888/Z/bs2cb3JywsjIceegiARYsWoVar+e6774wXeebNm4ezszMbNmzgscceY9asWbzzzjv069cPgG+++YY//vjjjt6rqiaBcyGEEELUKrpyPUkHMwEIbeIpNZGFeejKYa8yaReRj4OT/63bixuy1Kh5/dFIDAaD/CwLIYQQV+nUqRNff/01hYWFfPfddxw7doxXX33VuP6bb77hu+++49SpUxQVFVFaWkqjRo1M+oiJiTGpG+7j40NcXBwA8fHxWFhY0KxZM+P6OnXq4OzsbHwdHx9PQECAMWgOULduXZydnYmPjzcGzoODg43BWgAvLy80Go2xXMnlZVdnswO89dZbxMbGGl+7u7sb93s5U/yytm3bXleO5uqxA+zZs4ddu3aZlB+5fOGhsLCQ/v37M2vWLEJDQ+nSpQvdunWjZ8+eWFjcW3h18+bNJsd/bX+3+j5c1qBBA+NzlUqFt7e3yfu1ZMkSZs2axYkTJ8jPz6e8vPy6uurXfh98fHyMfcTHx1NSUsIjjzxyw2PYs2cPJ06cMNkeoLi4mMTERHJyckhLS6N169Ymx9msWbNqXa5FAudCCCGEqF1UENncm7QTF/ENf/DLWIhq6vgfkHsWtI5Q/0lzj6bGk6C5EEKIqqBVq5gTE2yW/d4pOzs7wsPDAfjiiy/o1KkTkydP5oMPPuDHH3/k9ddfZ8aMGbRu3RoHBwemT5/Ojh07TPq4drJMlUqFXq8HMAY7b/U3+GYXtq9dfqP93Grfl7m7uxuP8VrX7vdGY7GzM50fRa/XM3nyZPr27Xtdf9bW1gQEBJCQkMDatWtZt24do0aNYvr06WzcuPGeJhYNCQkxueBwrYq8F7dqs337dgYNGsTkyZN5/PHHcXJyYtGiRcyYMaPCfdjY2NzyGPR6PU2bNr1hnfura8/XNBI4F0IIIUStotGoCarnRlA9N3MPRdRWRRch7ifleaOnwEomtbxb+SXl2GvlXxohhBBVQ6VS3XHJlOpi4sSJdO3alZdeeonNmzfTpk0bY2kPUOpb34no6GjKy8vZvXu3sVxJQkICFy9eNLapW7cuKSkppKamGrPOjxw5Qk5OjklJl8oWHR3Nli1bGDJkiHHZ1q1bb7vPJk2akJCQcNNgPCgB5F69etGrVy9efvll6tSpQ1xcHE2aNMHKygqdTldpx1FZ/v77b4KCgnj33XeNy06dOnVHfURERGBjY8P69et5/vnnr1vfpEkTFi9ejKen53WZ7Jf5+Piwfft22rdvD0B5eTl79uyhSZMmdzSWqiSfMoUQQgghhKhKBxZBWRG4hkFop9u3FzdUptMzdvF+nGwsmdCzLo7Wd5/pJYQQQjzoOnbsSExMDNOmTSMiIoL//Oc//PHHH4SEhLBw4UJ27dpFSEhIhfuLioqiS5cujBgxgn//+99YWFgwZswYk8zkzp0706BBAwYPHsysWbOMk4N26NDhujIplemtt95iwIABNGnShEceeYTff/+dX375hXXr1t1yu/fff58ePXoQEBBA//79UavVHDx4kLi4OKZOncr8+fPR6XS0bNkSW1tbFi5ciI2NDUFBQYBS6mTTpk0MGjQIrVZrLB1zOxkZGcY69Je5ubndUxb71cLDw0lJSWHRokU0b96cFStW8Ouvv95RH9bW1owfP55x48ZhZWVF27ZtyczM5PDhwwwfPpzBgwczffp0evfuzZQpU/D39yclJYVffvmFt956C39/f1577TU+/vhjIiIiiI6O5rPPPjO50FIdqW/fRAghhBCi5jMYDOxbk0LqkQvodPrbbyDE/ZCVCCc3KM+bxsqEoPfgVFYhOr2BMp0eB8k6F0IIIW5r7NixzJkzhz59+tC3b18GDhxIy5YtycrKMsk+r6h58+YREBBAhw4d6Nu3LyNHjsTT09O4XqVSsXTpUlxcXGjfvj2dO3cmNDSUxYsXV+ZhXadPnz58/vnnTJ8+nZiYGL799lvmzZtHx44db7nd448/zvLly1m7di3NmzenVatWfPbZZ8bAuLOzM3PmzKFt27Y0aNCA9evX8/vvv+PmptzJOmXKFJKTkwkLC7uj8iRRUVH4+PiYPPbs2XPXx3+t3r178/rrr/PKK6/QqFEjtm7dyoQJE+64nwkTJvDGG2/w/vvvEx0dzcCBA4010G1tbdm0aROBgYH07duX6Ohohg0bRlFRkTED/Y033mDIkCHExsYaSwQ98cQTlXac94PKUJ0rsNcwubm5ODk5kZOTc9PbEh4kZWVlrFy5km7dulXaVTAhribnmKgN5DyvOudP57Pjt0TUFmoeHhKN1qb2BNpq3HlmMDyYAWWDAda8B1knILgdtHnF3COq9m517v5xOJ0fd6XSKMCZVx+JMNMIRU1X434/CnGX7vVcr23xDlAmNUxKSiIkJARra2tzD0cIUUnu5Ge79vzHKIQQQohaLXGfkg0REO1aq4LmNUrBeaX296m/IbA1NH4GrB+QCVwNBkhYpQTNLbTQ6Glzj6jGO5GRD0C4p72ZRyKEEEIIIR5EUqpFCCGEEA+8nMwizqfkgUpFSMOK1RoUVag4B/bMh99fU8qY6MogaRMsHwuJfypB55rsYgr8+QHsXaC8jukLtq7mHVMNZzAYSLwUOA+TwLkQQgghqqGuXbtib29/w8e0adPMPTxRAZJuJYQQQogH3sl9mQD4hDth56Q182iEUWkBHF0BR5dDeYmyzCtGmTAz/ne4eAp2fKsE05uPAOcAsw73jpXkKxn0x9eAQQ8aS4juDdG9zD2yGi+roJScojLUahXBbnbmHo4QQgghxHW+++47ioqKbrjO1VWSKGoCCZwLIYQQ4oFWmFvK2RMXAQhr7HnrxqJqlJfC8T/g8FIoVbKGcQ2Fhk+Bd32lvnlQG0hYCQd/hMwEWDUeontCvb5KqZPqTK9XMuUP/O/K8QW0gMZDwL7iE0WJm7tcpiXI1RYrC7mJVgghhBDVj5+fn7mHIO6RBM6FEEII8UA7uT8TDAbcAxxw8rAx93BqN125kj1+6GcouqAsc/SFBoOUwPLVE4KqNUqgPLA17J4HZ3bDkaVK/fPmw8G3sTmO4PYyE2D3XMhOVl47+kGz55QLAqLSeDla06mOJ54O1fwiihBCCCGEqLEkcC6EEEKIB5pPmBMFOSWENZZM30pTVgQn1kPSRqUEiaUNWNqBpTVY2l562Jh+LSuAI79BXrrSh6071H8SQtorQfKbsXOHDm9B6i4lIF2QCRs+VgLqTYZUn1rhhRdg33+VwD4ox9xgAIQ/Chr5yF3ZQtztCHGXEi1CCCGEEOL+kU/xQgghhHigufnZ4+YnkwdWiuIcSFgNx1ZDWeHd9aF1hJgnIOJRpeZ3RQU0B+96SumWhFWQsg3S9kODgeAadndjqSznDinZ8OUlgArCHoaGA8HaybzjEkIIIYQQQtw1CZwLIYQQ4oF0/nQ+rr52qNWq2zcWt5afoUzWefIv0JUpyxx8oE4PcPBSMtDLCpWvpQXK1/LiK8/LCkFfDn5NoU53JQv9bljaQNOhENIOdn4HFxJhz/xKO8x75h4BTZ8DNzMH8h9w5/NLyC4oJcjNTuqbCyGEEEKI+0YC50IIIYR4oOh1eo5uTydpfyahjT2JbuNj7iHVXNmnIH4ZnNqqlGQBJbu7bm/wbw5qMwUtXUPhsanKBKMn1oGu1DzjuMzSVrkgENzOtE67uC+2n8zi171naBHiygsd5CKFEEIIIYS4PyRwLoQQQogHRlF+Kfv+SCE7vQAAg8GAwWBAJcHMijMYIPOoUo/87L4ry73rQ90+4BVTPYLDajVEdVUeolY5kZEPQJiHlGASQgghqovY2FguXrzI0qVLAejYsSONGjVi1qxZZh2XuQUHBzNmzBjGjBlj7qHckZo6brj+XLwXEjgXQgghxAMhMyWPfWtTKCsux0KroeHDAXiHSo3pCjMY4PRuiP8Nzh+/tFAFgS2VDHPXULMOTwhQLoYlZioXxsI8JXAuhBBC3Ex6ejofffQRK1as4PTp0zg5OREREcEzzzzDkCFDsLW1va/7/+WXX7C0vIP5bCrgZgHRq5NkNBoNvr6+PPnkk3z00UdotdpKHcPNzJ8/nzFjxnDx4kWT5bt27cLOruomNN+wYQOdOnW64bq0tDS8vb2rbCwPAgmcCyGEEKJGM+gNHNt1jhN7MsBgwNHdhiZdgrBzqpoPyTVeeSkkb1ZqmOelKcvUFhDaEaJ7goN8uBbVR1pOMYUl5Vhq1AS43GWtfCGEEOIBd/LkSdq2bYuzszPTpk2jfv36lJeXc+zYMebOnYuvry+9evW6bruysrJKC3a7urpWSj8VNW/ePLp06UJZWRkHDhzgueeew87Ojg8++KBKx3EtDw8Ps+w3ISEBR0dHk2Wenp5mGUtNJrPpCCGEEKJGK8wrJelAJhgMBMa40aZfuATNK6IkHw7/CstegZ3/VoLmlrZKOZbes6HFCAmai2onMVMp0xLiYYeFRv6VEUIIIW5k1KhRWFhYsHv3bgYMGEB0dDT169enX79+rFixgp49ewJKpvY333xD7969sbOzY+rUqeh0OoYPH05ISAg2NjZERUXx+eefm/Sv0+kYO3Yszs7OuLm5MW7cOAwGg0mbjh07mpT5KC0tZdy4cfj5+WFnZ0fLli3ZsGGDcf38+fNxdnbmjz/+IDo6Gnt7e7p06UJampLYMWnSJBYsWMBvv/2GSqVCpVKZbO/s7Iy3tzcBAQH06NGDXr16sXfvXpMxff3114SFhWFlZUVUVBQLFy40WZ+SkkLv3r2xt7fH0dGRAQMGcO7cOeP6AwcO0KlTJxwcHHB0dKRp06bs3r2bDRs28Nxzz5GTk2Mc26RJkwCl5MnV5WpUKhXfffcdTzzxBLa2tkRERLBs2TKTcSxbtoyIiAhsbGzo1KkTCxYsQKVSXZfNfiuenp54e3ubPNSX5ieKjY2lT58+fPrpp/j4+ODm5sbLL79MWVmZSR+FhYUMGzYMBwcHAgMD+fe//22yfvz48URGRmJra0toaCgTJkww6WPSpEk0atSIhQsXEhwcjJOTE4MGDSIvL8/YRq/X88knnxAeHo5WqyUwMJAPP/zQuP7MmTMMHDgQFxcX3Nzc6N27N8nJycb1FTkX74V82hRCCCFEjWbnpKVBpwAadQ6kfkd/NBby8eaWCs7DngXw2yg4sAiKc8DWDZoMgT5fQaOnwMbF3KMU4oakvrkQQghzMhgMFJfpqvxxJ4HArKws1qxZw8svv3zTEiFXlzaZOHEivXv3Ji4ujmHDhqHX6/H39+fHH3/kyJEjvP/++/zjH//gxx9/NG4zY8YM5s6dy/fff8+WLVu4cOECv/766y3H9dxzz/H333+zaNEiDh48SP/+/enSpQvHjx83tiksLOTTTz9l4cKFbNq0iZSUFN58800A3nzzTQYMGGAMpqelpdGmTZsb7uvYsWP89ddftGzZ0rjs119/5bXXXuONN97g0KFDvPDCCzz33HP89ddfgPK97dOnDxcuXGDjxo2sXbuWxMREBg4caOxj8ODB+Pv7s2vXLvbs2cPbb7+NpaUlbdq0YdasWTg6OhrHdnncNzJ58mQGDBjAwYMH6datG4MHD+bChQsAJCcn8+STT9KnTx/279/PCy+8wLvvvnvL9/Zu/PXXXyQmJvLXX3+xYMEC5s+fz/z5803azJgxg2bNmrFv3z5GjRrFSy+9xNGjR43rHRwcmD9/PkeOHOHzzz9nzpw5zJw506SPxMREli5dyvLly1m+fDkbN27k448/Nq5/5513+OSTT5gwYQJHjhzh//7v//Dy8gKU86FTp07Y29uzadMmtmzZYrygUlpaahzjnZ6Ld0JKtQghhBCiRjEYDJzcn4mzpy1ufkrwzDfC2byDqgFsSjJR7/gKUneAQacsdA5UyrEEtgGNfCwU1d/ljPNwqW8uhBDCDErK9bz8w97bN6xk/xrcBGtLTYXanjhxAoPBQFRUlMlyd3d3iouLAXj55Zf55JNPAHj66acZNmyYSdvJkycbn4eEhLB161Z+/PFHBgwYAMCsWbN455136NevHwDffPMNf/zxx03HlJiYyP/+9z9Onz6Nr68voATCV69ezbx585g2bRqglIr55ptvCAsLA+CVV15hypQpANjb22NjY0NJSckN63Q/9dRTaDQaysvLKSkpoUePHrzzzjvG9Z9++imxsbGMGjUKgLFjx7J9+3Y+/fRTOnXqxLp16zh48CBJSUkEBAQAsHDhQmJiYti1axfNmzcnJSWFt956izp16gAQERFh7N/JyQmVSlWhGuKxsbE89dRTAEybNo0vv/ySnTt30qVLF7755huioqKYPn06AFFRURw6dMgkC7si/P39TV77+fmRkJBgfO3i4sLs2bPRaDTUqVOH7t27s379ekaMGGFs061bN+P7NX78eGbOnMmGDRuMx//ee+8Z2wYHB/PGG2+wePFixo0bZ1yu1+uZP38+Dg4OADz77LOsX7+eDz/8kLy8PD7//HNmz57N0KFDAQgLC+Ohhx4CYNGiRajVar777jvjxZ558+bh7OzMhg0beOyxx+74XLxT8h+SEEIIIWqM0uJyDqxPJSM5F62dJR2eisJSW7F/ImolgwHOHUZ9eCkNTq9FVeoJajV4xSgBc59GcFXGkRDV3XNtQ0jMyJfAuRBCCHEbqms+4+3cuRO9Xs/gwYMpKSkxLm/WrNl1237zzTd89913nDp1iqKiIkpLS2nUqBEAOTk5pKWl0bp1a2N7CwsLmjVrdtPM+L1792IwGIiMjDRZXlJSgpubm/G1ra2tMWgO4OPjQ0ZGRoWOd+bMmXTu3BmdTseJEycYO3Yszz77LIsWLQIgPj6ekSNHmmzTtm1bYxma+Ph4AgICjEFzgLp16+Ls7Ex8fDzNmzdn7NixPP/88yxcuJDOnTvTv39/k/FWVIMGDYzP7ezscHBwMB5nQkICzZs3N2nfokWLO97H5s2bjcFqUL5HV4uJiUGjufJ/lI+PD3FxcTcd5+WLAld/P5YsWcKsWbM4ceIE+fn5lJeXX1dXPTg42GQcV39P4+PjKSkp4ZFHHrnhMezZs4cTJ06YbA9QXFxMYmLiXZ2Ld0oC50IIIYSoES6eK2TvH6coyitFrVET0dwLCyspy3JDeh2k7oT4ZXDhJCq9HgMqDAGtoF4fcLvzD/hCVAdhHvZSpkUIIYTZaC3U/GtwE7Pst6LCw8NRqVQmJTUAQkNDAbCxMZ1c+9pyLj/++COvv/46M2bMoHXr1jg4ODB9+nR27Nhxl6NXso41Gg179uwxCdaCkkl+2bUTk6pUqgoHQL29vQkPDweULO28vDyeeuoppk6dalx+7cUEg8FgXHb185u1mTRpEk8//TQrVqxg1apVTJw4kUWLFvHEE09UaIy3Ok69Xn/TcdxNEDgkRC1zAwABAABJREFUJARnZ+e7GkNF2mzfvp1BgwYxefJkHn/8cZycnFi0aBEzZsyocB/XnovX0uv1NG3alB9++OG6dVU16aoEzoUQQghRrRkMBk4dyuLIlrMY9AZsHa1o0iUIJw9bcw+t+ikvgZMb+H/27js8inpr4Ph3d9N7r6RBCqH3Kk16E8SCghRFEBUQRUHlFSmCIFJUbNcGdr0WLioiIErvRUoCCSGQBNJ7z5Z5/1hYiLQEkmxCzud59mF36pllkuyeOXN+nPwVCi5Wg2gsURp25x8ba3p3GYXmXx9ehRBCCCFExahUqgq3TDEXd3d3+vbty6pVq5g6dep1+5xfz/bt2+nSpYupRQcYW61c4uzsjK+vL3v27KF79+4A6HQ6Dh48SJs2176o0Lp1a/R6PWlpaXTr1u0WjsrIysoKvV5foWUvJeiLi4sBiIyMZMeOHYwdO9a0zK5du4iMjASM1eUJCQkkJiaaqs6joqLIzc01LQMQHh5OeHg4zz77LA8//DCfffYZ9957b6Viu5HGjRuzfv36ctMOHDhw29utajt37iQoKKhc//Vz585VahuXBkD9888/efzxx6+a36ZNG7777ju8vLyuqmS/pLLnYmVJmZYQQgghai293sDhTQmc2HYexaDgHeLMXQ+GS9L830ry4Oh/Ye1TcOBTY9LcygGaPwDD3sPQ9lFKLV3MHaUQt2XjiRR2nc6goFRn7lCEEEKIWu29995Dp9PRrl07vvvuO6Kjozl16hRffvklJ0+evKrq+0qhoaEcOHCAP/74g5iYGF555RX2799fbplnnnmGxYsX8/PPP3Py5EmeeuopcnJyrrvN8PBwRo8ezdixY/npp5+Ij49n//79LFmy5Kok8Y0EBwdz9OhRTp06RUZGBlqt1jQvJyeHlJQULly4wNatW5k/fz7h4eGmpPcLL7zA6tWr+eCDD4iNjWX58uX89NNPpkE8+/TpQ4sWLRg9ejSHDh1i3759jB07lh49etCuXTuKi4uZMmUKf//9N+fOnWPnzp3s37/ftP3g4GAKCgr4888/ycjIoKioqMLHdaUnnniCkydPMmvWLGJiYvj+++9Ng3ZeqyL+etLS0khJSSn3uPL9ul2hoaEkJCTw7bffEhcXx9tvv13pQTltbGyYNWsWM2fO5PPPPycuLo49e/bwySefAMbBWD08PBg2bBjbt28nPj6erVu38swzz5CUlARU/lysLEmcCyGEEKLWUqtVoIBKrSKyqx9tBwZJT/Mr5afC/k/gf0/B8R+grADsPaHdYzD8PWh+P9hcuzpDiLpEb1BYe+Q8n+yIJ6eozNzhCCGEELVao0aNOHz4MH369OGll16iZcuWtGvXjnfeeYfnn3+eBQsWXHfdyZMnM2LECEaOHEnHjh3JzMwsV30OMGPGDMaOHcv48eNN7Vxu1q7ks88+Y+zYscyYMYOIiAjuuece9u7dW66n+M1MnDiRiIgI2rVrh6enJzt37jTNe/TRR/H19aVBgwY8/PDDNG3alN9//93U23v48OG89dZbLF26lKZNm/Lhhx/y2Wef0bNnT8CYlF67di2urq50796dPn360LBhQ7777jvAWMGemZnJ2LFjCQ8P58EHH2TgwIGmgVS7dOnC5MmTGTlyJJ6enrzxxhsVPq4rhYSE8MMPP/DTTz/RokUL3n//fVNVt7W1dYW3ExERga+vb7nHwYMHbymmaxk2bBjPPvssU6ZMoVWrVuzatYtXXnml0tt55ZVXmDFjBnPmzCEyMpKRI0eaeqDb2dmxbds2AgMDGTFiBJGRkTz22GMUFxebKtBv5VysDJVSVd3SBXl5eTg7O5Obm3vdWwjuJFqtlvXr1zNo0KCrehYJURXkHBP1gZzn12bQG1BrjNf3dWV68rNKcPWp3G2md7TMOGP/8oS9wMWPcm4NIfIeCOgA6vIXF+Q8E3XVpXO3WaeeLPo9BhsrDasebl2piishbkR+P4r64nbP9fqW7wDjAITx8fGEhIRgY2Nj7nBEPbZw4UI++OADEhMTzR3KHaEyP9vS41wIIYQQtYZeZ+D4tvPoyvS06R+ESqXCwkojSXMARYHkIxD9C6SeuDzdtxVEDgXvpiDJRFGLxWcUEuBqi8Wli2J6g+n5zcSlFwLQyMNekuZCCCGEuKO99957tG/fHnd3d3bu3MnSpUuZMmWKucOqlyRxLoQQQohaoTCnlIN/nCM/oxhUKnJSiyRhDqDXQcIuY8I8J8E4TaWBoC7GhLlrkHnjE+Im8kq0/HgwiR2xGdzXtgGDmvuSU1TGik0x9I70pnu45023cSbjYuLcy6G6wxVCCCGEMKvY2Fhee+01srKyCAwMZMaMGbz00ksADBw4kO3bt19zvZdffpmXX365JkO940niXAghhBBmlxyXyz9/JqLX6rGytaB1v0BJmmuL4fSfcGo9FGUap1lYQ6Pe0Hgw2HuYNz4hbsJgUNgak86Ph5IoLtMDkH2xP/meM5kkZRfz+e6z2FtraBvkdsNtXao4D5XEuRBCCCHucCtWrGDFihXXnPfxxx9TXFx8zXlubjf+PCUqTxLnQgghhDAbg95A9K5kzh7NAMDNz57W/YKwsa/HfWaLs+HUBojdCNoi4zQbZ4gYCKF9wVoSh6L2i0sv4Ms950jINJ7DAW52PNIpyJT47t/Uh9S8UrbFpPPh1jNM72NBE79r98wt1EJGQSkatZqGHnL+CyGEEKL+8vf3N3cI9YokzoUQQghhNoc3JZASlwtAw9ZeRHTyQa2up/2Ly4rgyFdw5m8w6IzTHH2N7ViCu4GFlVnDE6KiNkel8s0+Y1shWysNI9r40zPcq9zPtkqlYkynIApKdRw6l82qv2J5oX9jQjyuvtMks9T4r7+LLbZWmqvmCyGEEEIIUR0kcS6EEEIIswlp6UnWhUKa92qAT4izucMxn+yzsH05FKQaX3uEQeQwaNBOBvwUdU4zf2c0ahUdG7rzQLsGONlc+w4StVrFpO4NeWtzLNHJeazYFMOLAxvj52JbbrlABxjVqzlFupqIXgghhBBCCCNJnAshhBCixhgMCvmZxTh72gHg5mtPrzGNsbCsp1WkigJn/oIDn4JeC3bu0Okp8Glm7siEqLAz6QXEphXQv6kPAD7ONiy+rwVu9je/S8JSo2bK3aEs/eMUZzMKWbPrLC8ObIzqXxeMnGwtcbesxy2chBBCCCFEjZPEuRBCCCFqREmhlsObEshJLaLr/aE4uRurSutt0lxXCvs/gfitxte+raDLFLB2NGtYQlRUfomWnw6dZ3tsOgAR3o4EX2y1UpGk+SU2lhqe7RvO6p3xPNwh8KqkuRBCCCGEEOYgiXMhhBBCVLvM8wUc3phAaZEWjaWa4jytKXFeL+Wehx0rIDcRUEGLB6HpvdKWRdQJBoPCtth0fjp0nsJSY/+Uzo3cca1EsvzfHKwtmHJ3WLlpiqJwNrOQ9QlqrKLTGNhCBsMSQgghhBA1RxLnQgghhKg2iqIQdyidU3tTQFFwcLOh7YAgHFxtzB2a+ZzdCfs+NFac2zhDl2nSmkXUGfEZhXy55xxnMwoBaOBqyyOdggjzrto7JQ6ey2JbTAYN3W1JKoJTqQUMrNI9CCGEEMKcevbsSatWrVi5cmWN7G/16tVMnz6dnJycGtlfVTp79iwhISEcPnyYVq1amTucekVt7gCEEEIIcWcqK9Gx/7eznNqTDIqCf4QrXe8Prb9Jc70W9n8Mu942Js29msDAJZI0F3VGqU7Pys0xnM0oxMZKw8MdApkztGmVJ82LynR8tvMsx8/nsu5oMgCNPO2rdB9CCCHEnWz8+PGoVCoWL15cbvratWvrZEu0nj17olKprnpMnjzZ3KGJO5xUnAshhBCiWhzZnEj6uTzUFmqadfOnQaRrnfygXiUK0mDHSsiKM75uei80fwDU9bS/u6gzFEUBQKVSYW2h4d7W/pxOK+CBtgE421XPYJ12VhZMuTuUFZtiKNMZAEmcCyGEEJVlY2PDkiVLeOKJJ3B1da2x/Wq1WiyrYUDviRMnMn/+/HLT7Ozsqnw/QlxJKs6FEEIIUS3aDgiidf8gOt/biIAmbvU3aZ50EH6fZUyaWzlAzxeh5UOSNBe1XnxGIQt/i+ZIYo5pWs8ILx7v1rDakuaXNPZx4okejVChwkoNQW7yxVgIIYSojD59+uDj48Prr79+3WV27dpF9+7dsbW1JSAggGnTplFYWGiar1KpWLt2bbl1XFxcWL16NWBsIaJSqfj+++/p2bMnNjY2fPnll2RmZvLwww/ToEED7OzsaN68Od98881tHY+dnR0+Pj7lHk5OTuXi+Omnn+jVqxd2dna0bNmS3bt3X7WdP/74g8jISBwcHBgwYADJycmmefv376dv3754eHjg7OxMjx49OHToULn1VSoVH3/8Mffeey92dnaEhYWxbt26csucOHGCwYMH4+TkhKOjI926dSMuLs40/7PPPiMyMhIbGxsaN27Me++9V279ffv20bp1a2xsbGjXrh2HDx++rfdO3DpJnAshhBCiWmgs1PiFuuDiVY8TXmd3wraloC0C91Bjaxa/1uaOSogbKijV8cXusyz8LYr4jELWHj5vqjyvSW0CXZk9KIJ7ggxYWcjXFiGEELWAooC2pOYft/B3WKPRsGjRIt555x2SkpKumn/s2DH69+/PiBEjOHr0KN999x07duxgypQpld7XrFmzmDZtGtHR0fTv35+SkhLatm3Lr7/+yvHjx5k0aRJjxoxh7969ld52ZcyePZvnn3+eI0eOEB4ezsMPP4xOpzPNLyoq4s033+SLL75g27ZtJCQk8Pzzz5vm5+fnM27cOLZv386ePXsICwtj0KBB5Ofnl9vPvHnzePDBBzl69CiDBg1i9OjRZGVlAXD+/Hm6d++OjY0NW7Zs4eDBgzz22GOmOD766CNmz57NwoULiY6OZtGiRbzyyiusWbMGgMLCQoYMGUJERAQHDx5k7ty55WIUNUtatQghhBCiSl3Z2qFeO38Qdr8LKNCwF7R/HDTy0UvUXoqisD02gx8OJlFYavxy17mRO/e3bWC2n+dANzvcrM2yayGEEOJqulL477ia3+8Da8Cy8uME3XvvvbRq1YpXX32VTz75pNy8pUuXMmrUKKZPnw5AWFgYb7/9Nj169OD999/Hxqbi+5s+fTojRowoN+3KZO/UqVPZsGED//3vf+nYsWOljwPgvffe4+OPPy437d1332XcuMv/H88//zyDBw8GjMntpk2bcvr0aRo3bgwY28h88MEHNGrUCIApU6aUa/9y9913l9v+hx9+iKurK1u3bmXIkCGm6ePHj+fhhx8GMF2c2LdvHwMGDODdd9/F2dmZb7/91tSyJjw83LTuggULWLZsmen9CgkJISoqig8//JBx48bx1Vdfodfr+fTTT7Gzs6Np06YkJSXx5JNP3tL7Jm6PfHsTQgghRJXKulDI0b+TCGrqTsNWnuYOxzxST8COFaDoIbgbdHwC6vuFBFGrJWQW8cWes5xJN96e7ediyyOdgojwqdqBP4UQQghRs5YsWcLdd9/NjBkzyk0/ePAgp0+f5quvvjJNUxQFg8FAfHw8kZGRFd5Hu3btyr3W6/UsXryY7777jvPnz1NaWkppaSn29rc+Zsno0aOZPXt2uWleXl7lXrdo0cL03NfXF4C0tDRT4tzOzs6UNL+0TFpamul1Wloac+bMYcuWLaSmpqLX6ykqKiIhIeG6+7G3t8fR0dG0nSNHjtCtW7dr9nlPT08nMTGRCRMmMHHiRNN0nU6Hs7MzANHR0bRs2bJc//bOnTvf6K0R1UgS50IIIYSoUudjsinKKSU/q8TcoZhHZhxsXQJ6Lfi3g46TJWkuar3cYi1n0guxsdQwrJUfdzf2wkIj7VGEEEKIciysjdXf5tjvLerevTv9+/fn5ZdfZvz48abpBoOBJ554gmnTpl21TmBgIGC8g/Tf7dq0Wu1Vy/87Ib5s2TJWrFjBypUrad68Ofb29kyfPp2ysrJbPg5nZ2dCQ0NvuMyVyepLd8sZDIZrzr+0zJXHN378eNLT01m5ciVBQUFYW1vTuXPnq+K+1nYu7cfW1va68V1a5qOPPrqq8l6jMY5/ZI72eOL6JHEuhBBCiCqj1xlIjssFoEGEq5mjMYOcRPhrkfE2Xu+m0PUZac8iaiVFUUjOLcHPxfjlrnkDZ0a2D6BDiBsudlZmjk4IIYSopVSqW2qZYm6LFy+mVatW5VqGtGnThhMnTtwwGe3p6Vlu8MzY2FiKiopuur/t27czbNgwHnnkEcCYMI6Nja1UFbs5bN++nffee49BgwYBkJiYSEZGRqW20aJFC9asWYNWq70qwe7t7Y2/vz9nzpxh9OjR11y/SZMmfPHFFxQXF5uS8Hv27LmFoxFVQcpIhBBCCFFl0s7loyvVY+NgiZvfrd+KWSflp8JfC6GswDgQaPcXwEISkKJ6KYrCsaRcfj16gc1RqeyKyyAp+/IXWoNBoUSrL1e9dC6zkEXro1m4PpqcossVVP2a+kjSXAghhLgDNW/enNGjR/POO++Yps2aNYvdu3fz9NNPc+TIEWJjY1m3bh1Tp041LXP33XezatUqDh06xIEDB5g8efI1W5D8W2hoKJs2bWLXrl1ER0fzxBNPkJKSclvHUFRUREpKSrlHdnb2bW3z30JDQ/niiy+Ijo5m7969jB49+oYV5NcyZcoU8vLyeOihhzhw4ACxsbF88cUXnDp1CoC5c+fy+uuv89ZbbxETE8OxY8f47LPPWL58OQCjRo1CrVYzYcIEoqKiWL9+PW+++WaVHqeoOCmBEkIIIUSVOR9j/PDqF+ZatwYHVRQ48AmUFULEIPAIq9z6RVnw12tQnA3OAdDzRbCs3IdsISqjRKtn95lMNkelkpJbvi3S0JZ+NHA19sVMyy9l9s/HUKtV2FlpsLPSkJ5fiqKAtaWaxKxiSZYLIYQQ9cCCBQv4/vvvTa9btGjB1q1bmT17Nt26dUNRFBo1asTIkSNNyyxbtoxHH32U7t274+fnx1tvvcXBgwdvuq9XXnmF+Ph4+vfvj52dHZMmTWL48OHk5ubecvwfffQRH330Ublp/fv3Z8OGDbe8zX/79NNPmTRpEq1btyYwMJBFixaVG+S0Itzd3dmyZQsvvPACPXr0QKPR0KpVK7p27QrA448/jp2dHUuXLmXmzJnY29vTvHlz0yCtDg4O/PLLL0yePJnWrVvTpEkTlixZwn333VdlxykqThLnQgghhKgSZSU60s7mAeAf7mLeYCorYQ/EbjI+P7cLvJpA0+Hg0+Lm/clL842V5gVp4OAFvV4GaxlQUVSf3GIt/7f2OEWlOgBsrDS0auCCzqBQVKbD1/nyLeSFZcZlDAaFghIdBSXG1x1C3BjZPkCS5kIIIcQdaPXq1VdNCwoKoqSk/MX29u3bs3Hjxutux8/Pjz/++KPctJycHNPz4ODga/bkdnNzY+3atTeM8e+//77h/Mose604XFxcrupffmWPd4Dhw4eXW6Z169bs37+/3DL3339/udfXOt4r3xMwXpT49/t2pVGjRjFq1Kjrzu/UqRNHjhy56X5F9ZPEuRBCCCGqRHJcLopBwdHdFiePOlRtbdDD0e+Mz12DjX3K06KMD7eG0GQYNOgA6mt0uNMWw1+vQ24S2LrC3a+AnVuNhi/qh8yCUtwdjAODOdta4u9iS05RGX0ivbkrzAMbS80112voYc+7o9tQXKanSKunqFSHvbWFqbe5EEIIIYQQ4tokcS6EEEKIKuHsYUuDxm44e9WxhNzZHZCfDFYO0PtVYzL85C9w+k/IOgM7VoCjrzGBHtzt8mCfujLY+gZkxRnXvfv/jBXnQlQRvUHhcEI2m6JSOZdZxNIHWuBoY+wr+mSPRjjaWKBW3/iOCJVKhY2lBhtLDfVwuF4hhBBC1HLbt29n4MCB151fUFBQg9EIUZ4kzoUQQghRJVy87XDxtjN3GJWj18Gx/xqfNxkGVnbGR9vx0HQExGyAU78bE+t7PzAu23gINOwBu1YZq9ItbIztWZwbmPVQxJ3lWFIuX+w5S2aBcfBOjVpFbFoBbQKN6W9nu5sPzCWEEEIIUdu1a9fuqrYkQtQWkjgXQgghRP115m8oTAcbZwjvX36ejRO0eBAih8LpzXDyNyjKhENr4MhXYNCBxhJ6zAL3RmYJX9yZCkt1fLgtjuIyPQ42FvSM8KRXhJf0IxdCCCHEHcfW1pbQ0FBzhyHENV2jWWfN2bZtG0OHDsXPzw+VSnXVwAGKojB37lz8/PywtbWlZ8+enDhx4qrt7N69m7vvvht7e3tcXFzo2bMnxcXFpvnZ2dmMGTMGZ2dnnJ2dGTNmzFWN+xMSEhg6dCj29vZ4eHgwbdo0ysrKquOwhRBCiDuKoijEHkglJ7Wobg1aoyuD4z8anze9Fyysr72cpa0xeT70bWg/ERy8jUlzlQbueha8m9RczKJe2HA8heIyPX4utiy9vyX3tm4gSXMhhBBCCCFqmFkT54WFhbRs2ZJVq1Zdc/4bb7zB8uXLWbVqFfv378fHx4e+ffuSn59vWmb37t0MGDCAfv36sW/fPvbv38+UKVNQXzGA16hRozhy5AgbNmxgw4YNHDlyhDFjxpjm6/V6Bg8eTGFhITt27ODbb7/lxx9/ZMaMGdV38EIIIcQdIi+jhJi9Kez+OQ6d1mDucCru9CYozgI7dwjtc/PlLawgrA8MWQHdZ0Lf+eDftvrjFPVKTlEZm6JSARjRxh8rC7N+XBdCCCGEEKLeMmurloEDB153AABFUVi5ciWzZ89mxIgRAKxZswZvb2++/vprnnjiCQCeffZZpk2bxosvvmhaNywszPQ8OjqaDRs2sGfPHjp27AjARx99ROfOnTl16hQRERFs3LiRqKgoEhMT8fPzA2DZsmWMHz+ehQsX4uTkVC3HL4QQQtwJzsdkA+Ad4oSllcbM0VSQtgROrDU+b3afseVKRak10EAS5qJ6ONpYMrpTIMfP59EqwMXc4QghhBBCCFFvVaqE5dSpU8ydO5fevXvTqFEjfH19adGiBePGjePrr7+mtLS0ygKLj48nJSWFfv36maZZW1vTo0cPdu3aBUBaWhp79+7Fy8uLLl264O3tTY8ePdixY4dpnd27d+Ps7GxKmgN06tQJZ2dn03Z2795Ns2bNTElzgP79+1NaWsrBgwer7JiEEEKIO41iULgQkwOAX7iLWWOplJgNUJpnbLsS0sPc0QhholGr6BbmyZM9G6FSqcwdjhBCCCGEEPVWhSrODx8+zMyZM9m+fTtdunShQ4cODB8+HFtbW7Kysjh+/DizZ89m6tSpzJw5k+nTp2NtfZ0+oRWUkpICgLe3d7np3t7enDt3DoAzZ84AMHfuXN58801atWrF559/Tu/evTl+/DhhYWGkpKTg5eV11fa9vLxM+0hJSblqP66urlhZWZmWuZbS0tJyFwvy8vIA0Gq1aLXayh5ynXPpGOvDsQrzkHNM1Ad1/TzPSCqguLAMS2sNrr62deM4ygrRnFgLBgOGyOEoBgUMdSDu21DXz7P6wmBQUKslWX4lOXdFdZNzTNQXt3uuy8+IEKI+qlDifPjw4bzwwgt89913uLm5XXe53bt3s2LFCpYtW8bLL79cJQH+u9JGURTTNIPB2Ef1iSee4NFHHwWgdevW/Pnnn3z66ae8/vrr19zGv7dT0WX+7fXXX2fevHlXTd+4cSN2dnY3O7Q7xqZNm8wdgrjDyTkm6oO6ep7nx1tSmmmBjaeODX+cMXc4FdIgaxf+2fEUW7lx9EQuRK03d0g1pq6eZ/VBRglsTFLT3lMhzLkODbJbQ+TcFdVNzjFRX9zquV5UVFTFkYjaZu7cuaxdu5YjR46YO5Q64e+//6ZXr15kZ2fj4uJi7nAqTaVS8fPPPzN8+HBzh1KrVShxHhsbi5WV1U2X69y5M507d6asrOy2A/Px8QGM1eC+vr6m6Wlpaabq8EvTmzRpUm7dyMhIEhISTNtJTU29avvp6emm7fj4+LB3795y87Ozs9FqtVdVol/ppZde4rnnnjO9zsvLIyAggH79+tWLvuharZZNmzbRt29fLC0r0RtWiAqSc0zUB3X5PNdrDWz5/BR6bwMdh4Xg6lMHLhqX5qP57Uew9MLQdToNGnQwd0Q1whznmcGgkFVURlp+KRkFZeSX6GgT6IKvs02N7L+ueXvLaeyK8rAPcGPQXcHmDqfWqMu/I0XdIOeYqC9u91y/dIe9qFt27dpFt27d6Nu3Lxs2bKjy7QcHB5u6QqjVary9vRk4cCBvvvkmrq6uVb6/a7mVBPb48eNZs2bNVdP79+9fLe+TqLsqlDivSNL8dpa/lpCQEHx8fNi0aROtW7cGoKysjK1bt7JkyRLA+APq5+fHqVOnyq0bExNjGnS0c+fO5Obmsm/fPjp0MH453rt3L7m5uXTp0sW0zMKFC0lOTjYl4zdu3Ii1tTVt215/8C9ra+trtqSxtLSsVx+66tvxipon55ioD+rieV6UU4yltQYbe0s8GzjVjX7Mx38HfSm4N0Qd3AXqQsxVqKrPszKdgYJSHW72lz/7fbg1jrOZRWQWlKI3lK+c/vNUBkvvb4GNZR0ZRLaGnEzJIyq5AAuNhvvaBdS53wU1oS7+jhR1i5xjor641XNdfj7qpk8//ZSpU6fy8ccfk5CQQGBgYJXvY/78+UycOBG9Xk9MTAyTJk1i2rRpfPHFF1W+r6o0YMAAPvvss3LTbrfttLjzVGpw0GvZvHkzr776Kr/88kul1y0oKODIkSOm20Di4+M5cuQICQkJqFQqpk+fzqJFi/j55585fvw448ePx87OjlGjRgHG2wpeeOEF3n77bX744QdOnz7NK6+8wsmTJ5kwYQJgrD4fMGAAEydOZM+ePezZs4eJEycyZMgQIiIiAOjXrx9NmjRhzJgxHD58mD///JPnn3+eiRMn1ovKcSGEEOJWOHva0ntcEzre07BuJM2LsiDmd+PzFg/Wu6R5VUrOLeaznfFM+foQyzaWL2BIzSslLa8EvUFBo1bh42xDiwYuhHk7MqK1vyTN/0VRFH48mARAj3BPvBylIl8IIYQQt6+wsJDvv/+eJ598kiFDhrB69epy8xcvXoy3tzeOjo5MmDCBkpKScvP3799P37598fDwwNnZmR49enDo0KGr9uPo6IiPjw/+/v706tWLsWPHXrXcjz/+SNOmTbG2tiY4OJhly5aVm5+dnc3YsWNxdXXFzs6OgQMHEhsba5p/7tw5hg4diqurK/b29jRt2pT169dz9uxZevXqBRjHKlSpVIwfP75C74+1tTU+Pj7lHldWyatUKj7++GPuvfde7OzsCAsLY926dVdt5+DBg7Rr1w47Ozu6dOlSrrg3Li6OYcOG4e3tjYODA+3bt2fz5s3l1g8ODmbRokU89thjODo6EhgYyH/+859yyyQlJfHQQw/h5uaGvb097dq1K9c545dffqFt27bY2NjQsGFD5s2bh06nM82PjY2le/fu2NjY0KRJE2lPVgkVqji/5KmnnsLd3Z0FCxYAxhN/5MiRtGrViqVLl/Laa6+Va11yMwcOHDCd4IBp3XHjxrF69WpmzpxJcXExTz31FNnZ2XTs2JGNGzfi6OhoWmf69OmUlJTw7LPPkpWVRcuWLdm0aRONGjUyLfPVV18xbdo0+vXrB8A999zDqlWrTPM1Gg2//fYbTz31FF27dsXW1pZRo0bx5ptvVubtEUIIIeodtVqFvXPtrMyITc2nTG+gqZ+zcULU/0CvBY8w8Gtj3uDqqNNp+fx+LIUjiTmmaYWlunLjwjzQrgEAXo7WuNpZmQa7VJTy1edx6QUUlepp3sC5ZoKvpY4k5nAmvRArCzVDW/iZOxwhhBBC3ICiKJQZbr89cWVZqa0qXajy3XffERERQUREBI888ghTp07llVdeQaVS8f333/Pqq6/y7rvv0q1bN7744gvefvttGjZsaFo/Pz+fcePG8fbbbwOwbNkyBg0aRGxsbLm83JXOnz/Pr7/+SseOHU3TDh48yIMPPsjcuXMZOXIku3btMuUXLyW5x48fT2xsLOvWrcPJyYlZs2YxaNAgoqKisLS05Omnn6asrIxt27Zhb29PVFQUDg4OBAQE8OOPP3Lfffdx6tQpnJycsLW1reS7e33z5s3jjTfeYOnSpbzzzjuMHj2ac+fOlRv/cfbs2SxbtgxPT08mT57MY489xs6dOwFjwfCgQYN47bXXsLGxYc2aNQwdOpRTp06Vq/5ftmwZCxYs4OWXX+aHH37gySefpHv37jRu3JiCggJ69OiBv78/69atw8fHh0OHDpnGffzjjz945JFHePvtt+nWrRtxcXFMmjQJgFdffRWDwcCIESPw8PBgz5495OXlMX369Cp7j+50KuXf32JuIDIykrfffpu+ffsC0LVrV4YNG8bMmTP56aefmDlzJqdPn662YGu7vLw8nJ2dyc3NrReV6lqtlvXr1zNo0CC5bUtUCznHRH1QV8/zkkIt1rYWqNS1s2p7a0w6X+05h5WFmjlDmuClKYBfngGDDu5+BXyamTvEGnW751lMaj4/HkzidFqBaVrrQBcGNPOhkadDpb/IFZXpmLvuBJkFZfSO9Ob+tg2wsrjtGyHrHINBYc664yTnlDC4hS8j2jQwd0i1Tl39HSnqDjnHRH1xu+d6fct3AJSUlBAfH09ISAg2NsY7wkr1pcz4e0aNx7Ks5zKsNZUrVunatSsPPvggzzzzDDqdDl9fX7755hv69OlDly5daNmyJe+//75p+U6dOlFSUnLdwUH1ej2urq58/fXXDBkyBDBWSycnJ2NpaYler6ekpISOHTuyYcMGU7/x0aNHk56ezsaNG03bmjlzJr/99hsnTpwgNjaW8PBwdu7caWqpnJmZSUBAAGvWrOGBBx6gRYsW3Hfffbz66qtXxXWrPc6//PJL0//rJbNmzeKVV14BjBXn//d//2cqHi4sLMTR0ZH169czYMAA0343b95M7969AVi/fj2DBw+muLj4qm1f0rRpU5588kmmTJlieg8vXbwA48UZHx8f5s2bx+TJk/nPf/7D888/z9mzZ8sl7C/p3r07AwcO5KWXXjJN+/LLL5k5cyYXLlxg48aNDBo0iLNnz9KggfGz5oYNGxg4cGC9HRz0Wj/b11OhivN58+YBkJCQwP/+9z92796Noijs37+fli1bMn/+fEpKSkhISGD+/PkAzJkz5zYPQwghhBC11cHfz1JcoKV1v0Dc/RzMHY6JTm/g2/2J/HUyDYCmfs442VrCoR+NSXPvpvUuaV4VCkp1nE4rQKNW0bmROwOa+eDrfOvVPBZqNa0CXPkzOpU/o1M5lZLHxO4NaeBaBwaYrUJRyXkk55Rgb23BgGY+5g5HCCGEEHeIU6dOsW/fPn766ScALCwsGDlyJJ9++il9+vQhOjqayZMnl1unc+fO/PXXX6bXaWlpzJkzhy1btpCamoper6eoqIiEhIRy673wwguMHz8eRVFITEzk5ZdfZvDgwWzbtg2NRkN0dDTDhg0rt07Xrl1ZuXIler2e6OhoLCwsylWpu7u7ExERQXR0NADTpk3jySefZOPGjfTp04f77ruPFi1a3NZ71KtXr3IXDoCrEtNX7sPe3h5HR0fS0tKuu8ylcRPT0tIIDAyksLCQefPm8euvv3LhwgV0Oh3FxcVXvYdXbkOlUuHj42Paz5EjR2jduvU1k+ZgrOjfv38/CxcuNE27dBGjqKiI6OhoAgMDTUlzMP5fi4qpUOL80q0TH3zwAX379qVVq1Zs374dHx8fXnzxRRRFobCwkLffftv0wyKEEEKIO1Nhbik5qUWgql1tWvJLtLz/dxynUvIBuLeNP4Ob+5KfkUT24Q04WKlxbDHSzFHWfsVlerbGpGNtqaZXhBcArQNcuK9tA7o0csfF7vYHgbeyUDOqYyDN/J34dEc8SdnFvPZrNA+0a8Ddjb3qRs/8KtDM35lZAxuTW6zFzqpSHRSFEEIIYQZWaiuW9Vx28wWrYb+V8cknn6DT6fD39zdNUxQFS0tLsrOzK7SN8ePHk56ezsqVKwkKCsLa2prOnTtTVla+VY2HhwehoaEAhIWFsXLlSlMSvk+fPuVa+l0Zy7We/3uZS+s9/vjj9O/fn99++42NGzfy+uuvs2zZMqZOnVqhY7kWe3t7U9zX8++7M1QqlalFyrWWuRTvpWVeeOEF/vjjD958801CQ0OxtbXl/vvvv+o9vNF+btZ6xmAwMG/ePEaMGHHVPBsbm2u+v/Xls3ZVqNAn9KCgIMB428bSpUt5+umneeedd7j33ntNPXn2799PSEhItYzQK4QQQoja43yM8cO2R4ADNva147b2xKwi3tkSS2ZBGdaWaiZ2a0jrQOPgPnFbVqPKLeKsRws6e0aYOdLaK7uwjM3RqWyNSae4TI+jjQVdG3lgZaFGpVIxqLlvle+zRQMX5g1rxqc74jl+Ppev9yZw/HweT/RoWG8GEQ33vnaPUCGEEELUPiqVqtItU2qaTqfj888/Z9myZaax/i657777+Oqrr4iMjGTPnj2MHTvWNG/Pnj3llt2+fTvvvfcegwYNAiAxMZGMjIyb7l+jMX6GKy4uBqBJkybs2LGj3DK7du0iPDwcjUZDkyZN0Ol07N27t1yrlpiYGCIjI03rBAQEMHnyZCZPnsxLL73ERx99xNSpU7GyMl5U0Ov1FXp/atL27dsZP3489957L2DseX727NlKbaNFixZ8/PHHZGVlXbPqvE2bNpw6deq6FwGaNGlCQkICFy5cwM/POJ7O7t27K3cg9VilSltWrFjBmDFjmDRpEnfddVe53kIffvghQ4cOrfIAhRBCCFF7KIrC+VM5APiHu9544Rq0LTadzIIyvJysmXJ3GP4uFyszcpNoUnqUk8A6Q1dCckvwcb5xH7v6JjGriI1Rqew9k4neYKxI8XG2YWAzX2qihb2zrSXT+4TxZ3Qa/z2YiFoF1nd4v/MSrZ4Srb5KqveFEEIIIa7066+/kp2dzYQJE3B2Lj8I+/33388nn3zCiy++yLhx42jXrh133XUXX331FSdOnCg3OGhoaChffPEF7dq1Iy8vjxdeeOGa1c/5+fmkpKSYWrXMnDkTDw8PUxJ8xowZtG/fngULFjBy5Eh2797NqlWreO+99wBjlfqwYcOYOHEiH374IY6Ojrz44ov4+/ubWrxMnz6dgQMHEh4eTnZ2Nlu2bDEl1YOCglCpVPz6668MGjQIW1tbHBxu3kqytLSUlJSUctMsLCzw8PCoxLt9Y6Ghofz0008MHToUlUrFK6+8clXF+s08/PDDLFq0iOHDh/P666/j6+vL4cOH8fPzo3PnzsyZM4chQ4YQEBDAAw88gFqt5ujRoxw7dozXXnuNPn36EBERwdixY1m2bBl5eXnMnj27yo7xTlepbyXBwcFs376d/Px8fv/993JXOj7++GMWLVpU5QEKIYQQovbITSumKLcUjYUan4a1Z2Coke0C6N/Uh9mDm1xOmhsMcOQbrC3UFHq3Jc3Sn83RqeYNtJbZcDyFuetOsOt0BnqDQriPI9N6h/Ha8GbcFeaBhaZmEtgqlYo+Tbz5v8FNGNc12HT7qE5vuCNbAG44nsKLPx7jTzkfhRBCCFHFPvnkE/r06XNV0hyMFedHjhwhLCyMOXPmMGvWLNq2bcu5c+d48sknyy376aefkp2dTevWrRkzZgzTpk3Dy8vrqm3OmTMHX19f/Pz8GDJkCPb29mzatAl3d3fAWBH9/fff8+2339KsWTPmzJnD/PnzTW2hAT777DPatm3LkCFD6Ny5M4qisH79elMLE71ez9NPP01kZCQDBgwgIiLClHj39/dn3rx5vPjii3h7e5sG3byZDRs24OvrW+5x1113VWjdilqxYgWurq506dKFoUOH0r9/f9q0aVOpbVhZWbFx40a8vLwYNGgQzZs3Z/HixabK/v79+/Prr7+yadMm2rdvT6dOnVi+fLmpe4harebnn3+mtLSUDh068Pjjj5frhy5uTKXcid9GzKS+jTItI9CL6ibnmKgP6tp5fmRzAudPZeMX5krrfuZrz1ai1bPlZBoDmvqgvlZZdHEO7F4FKccAFbFt57B4dyHWlmrefKBlvesnfek869d/AHqVBgdr4/EnZRcxd10UbYNc6d/Um4aetWegV0VR+GRHPABjOwdjdYdUoeeVaHnxx6OUag081asRbYOuPdCTMKprvyNF3SPnmKgvbvdcr2/5DoCSkhLi4+MJCQnBxkbuWBTiTlGZn+0KfQNZvHgxRUVFFdr53r17+e233yq0rBBCCCHqDm2ZnoykAgAaNDZfm5aMglJeXx/NjweT+Onw+asXSD0Bv88yJs01VtD5aULDI/F1saFUa2BH7M17M95pisv0HM1SMft/UXy3P9E0vYGrHcseaMmTPRvVqqQ5QGJWMXvOZLE7LpPXf48ms6DU3CFViV//SaZUayDYw542gbWn3ZEQQgghhBCivAolzqOioggMDOTJJ5/k999/Jz093TRPp9Nx9OhR3nvvPbp06cJDDz1Ub64+CiGEEPWJpZWGNv2DiOjki0eAeZKsp1LyWfBrFEnZxTjZWtIq4IpbUA0GOP4j/LkASnLAyR8GvA4h3YytQCK9AdhyMg2DoeZvuDPHTX7ZhWV8fyCRF38+zp40FdlFZUQn51Gmu9xb0dmudlZYBrrbMaNfOPbWFiRkFrHg1yhOpeSbO6zbkp5fyt+n0gC4v20DU0saIYQQQghRNRISEnBwcLjuIyEhwdwhijqkQvcpf/755xw9epR3332X0aNHk5ubi0ajwdra2lSJ3rp1ayZNmsS4ceOwtq7dIwwLIYQQomJSz+ah1xnwC3UBwM3XHjdfe7PE8tfJNL7el4DBoBDkbs+Uu0Nxs784uGJJLux652JrFiCkB7R7DCwv33rXqaE76/65QISPIyU6fbW3azmXWUhhqZ4mfk6U6Qy8syWWrqEedGroXq37BWMLlg3HU9gbn4XBoGBQDLhYwdhOQXQN86ozbU8ifZ2YM7QJq7acJjGriDc3nmJUh0B6RnjWuaTz6bR8vtyTgN6g0MTPiUhfKTQRQgghhKhqfn5+HDly5IbzhaioCn9jbNGiBR9++CEffPABR48e5ezZsxQXF+Ph4UGrVq2qdNRZIYQQQpiXYlCI2ZfC6YNpaCzUOLnb4uBqngvjOr2Br/clsPWU8Y63jg3dGN8l5HLyNzUKdr0NxdmgsYT2j0PDnldtx8ZSwxv3taiRAS/zSrSs2nKa7KIynuwZSlZhGVEX8ohOzqNEq6dnxNUDK1WlI4k57I7LBCDcx5E+ER4k/pPCXaHuWNaRpPklHg7WvDSoMZ/tPMv++Cy+3HOO9PxSHmwfYO7QKqxUp+edLacpKNFhY6XhwXZ1J3YhhBBCiLrEwsKC0NBQc4ch7hCVLrVSqVS0bNmSli1bVkc8QgghhDCz0mIdhzeeI/NiP/OAJm7YOZmvnUdafim74zJRqeC+Ng0Y0MzHWG2sKHDiZzj6PaAYW7Pc9Sy4XD8pWRNJc71B4cOtcWQVluHlZE2kryO2lhpS8kr4+2QaX+w+R4nWwIBmPlWyP53ewL6zWbjaWZmqmHtGeJGcU0LvSC8aejqg1WpJOloluzMLawsNT3RvSJCbHT8fPk8zf+ebr2RmZToDlhoVKpUKawsN97b2Jz6jkBGtG9Ta9jhCCCGEEEKIy6r3HmUhhBBC1ClZyYUc/uMcJYVaNBZqWtzdAL8w8w5g6Odiy+PdQrBQq2kZ4GKcWJILu1ZBysVscEh3aDehXGuWGzmXWcj57GK6hFb9HXP/PZDIyeR8rC3VTLk7zNQS5pGOgdhaavj9WDL/PZBIqU7PPS39brnlSHGZnq0x6WyOTiW7sIxQLwdT4tzB2oKJ3RtW2THVBiqVioHNfenY0P1yix6gRKvHxlJjxsjKUxSFA+ey+X5/Ig91CKRtkPHnp2eEFz0jzBycEEIIIYQQosIkcS6EEEIIAOKPZhC98wKKQcHe1Ya2A4JwdKtYIrqq7T+bhZu9FY08jYOQtg1yuzyzgq1Zric+o5DXfo3CysKYiLe3rrqPQ7vjMtkUlQrAhLtC8HexNc1TqVTc37YBNpZqfj50nnVHLlCi1fNgu4BKJc+zCsvYHJXK1th0Ssr0ADjZWtK8gTMGg4JaXbd6f1fWlUnzlNwSFvwWhaudJfZWFthZWWBvrcHWSoO9lQVtg1wJcLMDoLBUR2ZBGXbWGuysNNhaaqq8T3pCZhFf70sgNtU4iOnm6FRT4lwIIYQQQghRt0jiXAghhBAAlBZqUQwKvqEutOjVAAurmq/iVRSFtUfO8+s/yTjbWvLqPU1xtrW8NBOi1sI/32FszeJ3sTVLYKX2EexuRwNXW5Kyi9kem1FlLVMSMotYs+ssAINb+JZP9l9hSAs/bCw0fLMvgR2nM+kT6Y27Q8X6x/969AL/O3IBg0EBwMfZhgHNfOgY4l5nBvysSttijBcPki9eQPg3f1dbU+I8OjmP9/+OM81TqcDWygI7S2Oi/d7W/qY7GlLzStgXn4WdlQY7KwvsrDTYW19+7mBtUa7tT26xlp8PJbHjdAaKApYaNQOb+1TZuSWEEEIIIYSoeZI4F0IIIQQA4R19cPK0xbeRc5VX4lZEiVbPR9vOcCQxB4BODd1xuFQNXpILu9+D5CPG18HdjJXmFWzNciWVSkXvSG/W7DrLlpOp9GviXSVV2vvOZqHVG2jm78zwVv43XLZPE29srTT4OtvcMGmuKAp6g2JK0no72WAwKIT7ODKgqQ8tGpjn/6q2eKBdA+4K8yC3WEtRmY6iMj2FpXrTcx+ny+eHAjjbWlJUpkerN6AoUFSqo6hUB0CZ3mBaNjGriLWHz193v2M6B5kGeD2dVsDSP06i0xsvZnQIceOBdgHlKuOFEEIIIYQQdc8tJ85Pnz5NXFwc3bt3x9bWFkVR6vUXNyGEEKKuU6tV+IW6mGXfaXklvLPlNBdyirHQqBjXOfhy//G0k7DzLSjOMrZmafcYNOxlLBm+RR0buvHDwSQyC8r4JymH1oG3307jvjb+eDtZ0ybQtUKJ+K7/6q+emFWEt5MNVhZq04CffxxPoVNDdwY29wWgTaArrwxpQrCH/W3HeydQqVT4udjid0VLnOtpH+xG+2DjXQBlOgPFZXqKtDoKS/UUl+kJcLu8DVd7K7qFeVBYZpxXVHY5GV9UpivX3ie3uAydXiHI3Z6HOwQQ5u1Y9QcqhBBCCCHMomfPnrRq1YqVK1eaO5RKmzt3LmvXruXIkSPmDqXOqnTiPDMzk5EjR7JlyxZUKhWxsbE0bNiQxx9/HBcXF5YtW1YdcQohhBCiGmSeLyBmXyq+oc4EN6/6gTIrIupCHu9vjaOoVIeznSVP9wo19jZXFIj6Hxz9DhQDOPoaW7O4Bt32Pq0tNHQP9+T3Y8lsjk69rcT5peIBlUpFtzDPW9pGfEYhb/5xihAPe5r5O5sG/ATYftrYTkalUqFRqyRpXgWsLNRYWahxxvKa8xt5Opj66/+boigoSvllZw1sTJiXgxSRCCGEEMLs9Ho93bp1w9fXlx9//NE0PTc3l2bNmjFu3Dhee+01AH788UfeffddDh8+TGlpKQEBAXTt2pWpU6fSunVrAFavXs2jjz5q2o69vT0RERHMnj2bESNG1NhxVTaBffbsWUJCQq45b/fu3XTq1KkKoxN3qko3w3z22WexsLAgISEBOzs70/SRI0eyYcOGKg1OCCGEENUrPSGfrAsF5KQWmS2GrTHpFJXqCPGwZ86QJsaEZUke/L0Y/vnGmDQPvgsGLK6SpPklvSI8UalUnEzOJyn71o5/z5lM3v7zNEVlutuKpVSnR0EhOjmP/x5IJLuwDCdbS+5t48/LgyIlIVuLqFSqcncUuNhZEe7tKP9HQgghhKgVNBoNa9asYcOGDXz11Vem6VOnTsXNzY05c+YAMGvWLEaOHEmrVq1Yt24dJ06c4D//+Q+NGjXi5ZdfLrdNJycnkpOTSU5O5vDhw/Tv358HH3yQU6dO1eix3YrNmzebYr/0aNu2rbnDEnVEpRPnGzduZMmSJTRo0KDc9LCwMM6dO1dlgQkhhBCi+mUkFgDgEWC+9hKPdg1mSEtfZg5ojIudFaSfgt9nGfuZayyhwyToPOWW+pnfiLuDNW2CXHCxsyK7UFvp9RMyi1i98yxHk3LYFpNxW7E09nFiRr8InO0s8XOxZXzXYN64vwVDWvhd7vMuhBBCCCFEBYSFhfH6668zdepULly4wP/+9z++/fZb1qxZg5WVFXv27OGNN95g+fLlLF++nG7duhESEkKPHj2YPXs269evL7c9lUqFj48PPj4+hIWF8dprr6FWqzl69KhpmezsbMaOHYurqyt2dnYMHDiQ2NjYctv58ccfadq0KdbW1gQHB1/VteK9994jLCwMGxsbvL29uf/++wEYP348W7du5a233jLd6Xn27NkKvRfu7u6m2C89LC2Ndx3OnTuXVq1a8cUXXxAcHIyzszMPPfQQ+fn55bZhMBiYOXMmbm5u+Pj4MHfu3HLzly9fTvPmzbG3tycgIICnnnqKgoIC0/zVq1fj4uLCH3/8QWRkJA4ODgwYMIDk5ORy2/n0009N74+vry9TpkwxzcvNzWXSpEl4eXnh5OTE3XffzT///FNu/cWLF+Pt7Y2joyMTJkygpKSkQu+RuL5KJ84LCwvLVZpfkpGRgbX19Qe3EkIIIUTtUlaiIzejGACPBtduS1Edcou1/Hr0AsrFfhc2lhrubd0AK43K2Jpl81xjP3NHX+i3EEJ731Y/8xt5pFMQS+5rTvMGzpVaL79Ey6q/Yk2DgfZr4n3bsTTydGDZAy1ZMLwZ3cI8sdRU+mOaEEIIIYSoRoqiYCgtrfGHcmWfuAqaOnUqLVu2ZOzYsUyaNIk5c+bQqlUrAL755hscHBx46qmnrrnuje6k0+v1rFmzBoA2bdqYpo8fP54DBw6wbt06du/ejaIoDBo0CK3WWKBy8OBBHnzwQR566CGOHTvG3LlzeeWVV1i9ejUABw4cYNq0acyfP59Tp06xYcMGunfvDsBbb71F586dmThxoqlqPCAgoNLvybXExcWxdu1afv31V3799Ve2bt3K4sWLyy2zZs0a7O3t2bt3L2+88Qbz589n06ZNpvlqtZq3336b48ePs2bNGrZs2cLMmTPLbaOoqIg333yTL774gm3btpGQkMDzzz9vmv/+++/z9NNPM2nSJI4dO8a6desIDQ0FjOfd4MGDSUlJYf369Rw8eJA2bdrQu3dvsrKyAPj+++959dVXWbhwIQcOHMDX15f33nuvSt6j+qzSJUzdu3fn888/Z8GCBYDxh8lgMLB06VJ69epV5QEKIYQQonpkJBWAouDgZoON/bV7PVe1sxmFrPrrNNmFZVio1Qxo5mOcUZoPu9+DC4eMr4O6GCvNLW8+6OPtcLKp3HHnFJVx4kIeW06mkVlQhpeTNZO6N6zQYKAVIe0+hBBCCCFqL6WsjPPPTK/x/fq/tRJVJYtVVSoV77//PpGRkTRv3pwXX3zRNC8mJoaGDRtiYXE5Lbh8+XJTGxeA8+fP4+xsLC7Jzc3FwcFYaFNcXIylpaWprQtAbGws69atY+fOnXTp0gWAr776ioCAANauXcsDDzzA8uXL6d27N6+88goA4eHhREVFsXTpUsaPH09CQgL29vYMGTIER0dHgoKCTH3WnZ2dsbKyws7ODh8fn0q9D126dEGtLl+Qkpubi0ajAYzV5KtXr8bR0XgH7pgxY/jzzz9ZuHChafkWLVrw6quvAsZq/lWrVvHnn3/St29fAKZPn25aNiQkhAULFvDkk0+WS1xrtVo++OAD03s2ZcoU5s+fb5r/2muvMWPGDJ555hnTtPbt2wPw119/cezYMdLS0kxFy2+++SZr167lhx9+YNKkSaxcuZLHHnuMxx9/3LS9zZs3S9X5bap04nzp0qX07NmTAwcOUFZWxsyZMzlx4gRZWVns3LmzOmIUQgghRDXISDTeguhZQ21a9p7J5LOdZ9HqDXg729AqwMU4I/0U7HwLijJBbQFtH63WKvNr0RsUjibl0KKBC5orkuCXBv4EY5X58//9xzQwpLWlmqd7hWIvrVSEEEIIIUQt9Omnn2JnZ0d8fDxJSUkEBweb5v27YOOxxx7jnnvuYe/evTzyyCPlqtwdHR05dMhY4FJUVMTmzZt54okncHd3Z+jQoURHR2NhYUHHjh1N67i7uxMREUF0dDQA0dHRDBs2rNw+u3btysqVK9Hr9fTt25egoCAaNmzIgAEDGDBgAPfee+81u15UxnfffUdkZGS5aZeS5gDBwcGmpDmAr68vaWlp5ZZv0aJFudf/Xuavv/5i0aJFREVFkZeXh06no6SkhMLCQuzt7QGws7MzJc3/vY20tDQuXLhA7969r3kMBw8epKCgAHd393LTi4uLiYuLA4zv7+TJk8vN79y5M3/99dc1tykqptLf9Jo0acLRo0d5//330Wg0FBYWMmLECJ5++ml8fX2rI0YhhBBCVDFFUa7ob169bVoMBoWfDp/n92PGHn7NGzgzqXtD7Cw1ELXu8gCgjj5w17PgGlyt8fyboigs/C2ac5mFPNWrEX4uthw/n8fx87lYalRMuTsMAEcbSxq42qFWqWjm70SXRh74OFdt33UhhBBCCFF7qays8H9rpVn2W1m7d+9mxYoV/P7777zxxhtMmDCBzZs3o1KpCAsLY8eOHWi1WlO/bxcXF1xcXEhKSrpqW2q12tQ2BIyJ5EtjIA4dOvS6rWSuLEK58vmV8y+5lJz/+++/2bhxI3PmzGHu3Lns378fFxeXSh//JQEBAeVi/7dLx3/Jpc4aFV3m3LlzDBo0iMmTJ7NgwQLc3NzYsWMHEyZMMLWpud42Lh2/re2N77I1GAz4+vry999/XzXvdt4bcXO3VCLl4+PDvHnzqjoWIYQQQtQQvc6Ao7sNOq0BNz/7attPUZmOj7bFczQpB4CBzX0Z0doftbYAttZ8a5ZrUV1MhJ/LLOSDrWcwGC5/gLfQqCjV6bG2MFalzB4cKb3HhRBCCCHqKZVKVemWKeZQXFzMuHHjeOKJJ+jTpw/h4eE0a9aMDz/8kMmTJ/Pwww/zzjvv8N5775VrDVIZGo2G4mLjeElNmjRBp9Oxd+9eU6uWzMxMYmJiTNXeTZo0YceOHeW2sWvXLsLDw00V4BYWFvTp04c+ffrw6quv4uLiwpYtWxgxYgRWVlbo9fpbfUuqzYEDB9DpdCxbtszUEub777+v1DYcHR0JDg7mzz//vGYb7DZt2pCSkoKFhUW5uwauFBkZyZ49exg7dqxp2p49eyoVh7hapRPn27Ztu+H8S437hRBCCFF7WVhqaD845JqVH1XpQk4xxy/kYqlRM75rMJ0aukNGLOxYYdbWLP/WK8KLjSdS0eoNWGhUhHs70tTPmaZ+TlhdkSiXpLkQQgghhKjtXnzxRQwGA0uWLAEgMDCQZcuW8dxzzzFgwAA6d+7MjBkzmDFjBufOnWPEiBEEBASQnJzMJ598gkqlKtcXXFEUUlJSAGNSftOmTfzxxx+mnuhhYWEMGzaMiRMn8uGHH+Lo6MiLL76Iv7+/qT3LjBkzaN++PQsWLGDkyJHs3r2bVatWmfqA//rrr5w5c4bu3bvj6urK+vXrMRgMREREAMaWKnv37uXs2bM4ODjg5uZ2Ve/ya8nMzDTFfomLiws2NlVz52ijRo3Q6XS88847DB06lJ07d/LBBx9Uejtz585l8uTJeHl5MXDgQPLz89m5cydTp06lT58+dO7cmeHDh7NkyRIiIiK4cOEC69evZ/jw4bRr145nnnmGcePG0a5dO+666y6++uorTpw4QcOGDavkOOurSifOe/bsedW0K79w18arP0IIIYS4tuoejDLUy5FHuwbj62xLiLsdRP8CR74BRQ8O3sbWLG4h1RpDRbjaWzF7cCS5xVrCvB1MFeZCCCGEEELUJVu3buXdd9/l77//NvXXBpg4cSI//PCDqWXLm2++SYcOHXj//ff59NNPKSoqwtvbm+7du7N7926cnJxM6+bl5ZnaM1tbWxMUFMT8+fOZNWuWaZnPPvuMZ555hiFDhlBWVkb37t1Zv369qUVJmzZt+P7775kzZw4LFizA19eX+fPnM378eMCYzP7pp5+YO3cuJSUlhIWF8c0339C0aVMAnn/+ecaNG0eTJk0oLi4mPj7+utXXV+rTp89V07755hseeuihSr+319KqVSuWL1/OkiVLeOmll+jevTuvv/56ucrvihg3bhwlJSWsWLGC559/Hg8PD+6//37A+J1t/fr1zJ49m8cee4z09HR8fHzo3r073t7eAIwcOZK4uDhmzZpFSUkJ9913H08++SR//PFHlRxnfaVSrteI6Dpyc3PLvdZqtRw+fJhXXnmFhQsXXreRfX2Ql5eHs7Mzubm55X7B3Km0Wi3r169n0KBBV/VqEqIqyDkm6gNznOcGg0JJgRY7p8r3Srxlpfmw5304f9D4OrATdHgCrG5vsB9RMfL7VNRVcu6K6ibnmKgvbvdcr2/5DoCSkhLi4+MJCQmpsupkIYT5VeZnu9IV587OzldN69u3L9bW1jz77LMcPHiwspsUQgghRA3KTSti14+ncfa24677w6plH9tj03G0saSZnxMW2XGwYyUUZRhbs7QZB2F9zdqaRQghhBBCCCGEuJEqa9Tp6enJqVOnqmpzQgghhKgmGYkFANg6VE9lnU5v4L8HknhncwwX9v4Im141Js0dvKHfaxDeT5LmQgghhBBCiFs2efJkHBwcrvmYPHmyucMTd4hKV5wfPXq03GtFUUhOTmbx4sW0bNmyygITQgghRPXISMoHwCPAsVq2f+x8LoWlOgaWricgPsqYJJfWLEIIIYQQQogqMn/+fJ5//vlrzqsv7YRE9at04rxVq1aoVCr+3Rq9U6dOfPrpp1UWmBBCCCGqnq5MT3ZKEQAeDRyqZR+74jKx0+fT2XAYlcoG2j4K4f2lylwIIYQQQghRJby8vPDy8jJ3GOIOV+nEeXx8fLnXarUaT09PGShBCCGEqAMyLxSiGBTsnKywd7au8u0Xlur4JzGH5qVHcbOzBPdQiBhQ5fsRQgghhBBCCCGqU6UT50FBQdURhxBCCCFqQEZi9bZp2X82C71Bob1yHFsrDYT0qJb9CCGEEEIIURP+3XFBCFG3VeZnukKJ87fffrvCG5w2bVqFlxVCCCFEzbo0MGh1Jc53n8nEXZdKQ4t0UDtAUOdq2Y8QQgghhBDVydLSEoCioiJsbW3NHI0QoqoUFRlbl176Gb+RCiXOV6xYUaEdq1QqSZwLIYQQtZSiKIR39CEjMR93f/sq336ZzkCp1kDjkiO4OFuBbyuwrp4EvRBCCCGEENVJo9Hg4uJCWloaAHZ2dqhkzB4h6ixFUSgqKiItLQ0XFxc0Gs1N16lQ4vzffc2FEEIIUfeoVCp8Gznj28i5WrZvZaFm7pBIin98CyutGkK6V8t+hBBCCCGEqAk+Pj4ApuS5EKLuc3FxMf1s30yle5wLIYQQQlxX2glstblgaQf+bcwdjRBCCCGEELdMpVLh6+uLl5cXWq3W3OEIIW6TpaVlhSrNL7mlxHlSUhLr1q0jISGBsrKycvOWL19+K5sUQgghRDVSFIW4Q+m4+dnj6m2HSl21t5nmlWix0qixid9unBDUBTQ37xknhBBCCCFEbafRaCqVbBNC3BkqnTj/888/ueeeewgJCeHUqVM0a9aMs2fPoigKbdpIZZkQQghRG+VnlnBqTzIaCzV9H2+KhqpNnP/yzwX2xFzglbKteNkCwd2qdPtCCCGEEEIIIURNUld2hZdeeokZM2Zw/PhxbGxs+PHHH0lMTKRHjx488MAD1RGjEEIIIW5TRmIBAG5+9mg0lf7zf0M6vYF98Vk0KDyBDVqw9wTPiCrdhxBCCCGEEEIIUZMq/c05OjqacePGAWBhYUFxcTEODg7Mnz+fJUuWVHmAQgghhLh96Un5AHgEOFb5to9fyKOgREdL3TEcbSyMg4KqqraiXQghhBBCCCGEqEmVTpzb29tTWloKgJ+fH3FxcaZ5GRkZVReZEEIIIaqEXmcg60IhAB4BDlW+/d1xmdjp82muPotKpZI2LUIIIYQQQggh6rxK9zjv1KkTO3fupEmTJgwePJgZM2Zw7NgxfvrpJzp16lQdMQohhBDiNmSnFGHQGbC2t8TRzaZKt11UpuNIYjbNSo/iZmcJ7qHg5Ful+xBCCCGEEEIIIWpahRPn6enpeHp6snz5cgoKjH1S586dS0FBAd999x2hoaGsWLGi2gIVQgghxK3JSLzYpqWBg7EivAodOJuNTq/QznAcGys1hPSo0u0LIYQQQgghhBDmUOHEub+/P/fccw8TJkxgwIABANjZ2fHee+9VW3BCCCGEuH3ZKUXA9fubn0rJ52hSDoOa+2JvXbmb0XafycRNl0pDi3RUagcI6nzb8QohhBBCCCGEEOZW4R7na9asIS8vj6FDhxIQEMArr7xSrr+5EEIIIWqnjsMa0uW+ULyCrp04/2LPWTYcT+GPEymV3vbjd4Uw2jsBV3sr8G0F1lU/+KgQQgghhBBCCFHTKpw4f/jhh9m4cSPx8fFMnDiRr776ivDwcHr16sVXX31FSUlJdcYphBBCiFukVqtw9bHHyubqavKcojKSc4x/w/s19an0tt3trWilP46VRg0h3W87ViGEEEIIIYQQojaocOL8koCAAF599VXOnDnDxo0b8ff3Z9KkSfj6+vLUU09VR4xCCCGEqCYnU4z9zwPd7XC42KZFqzeQU1RWsQ2knoCiTLC0A/821RWmEEIIIYQQQghRoyqdOL9S7969+fLLL/n8889Rq9V8+OGHVRWXEEIIIW6Toijs/jmOo38lUVqkveYy0cl5AET6OAGgNyj8Z9sZFv4WTVre9e8mi88oZMWmGBIPbTBOCOoCGsuqPYDaqjATMuOgJNfckQghhBBCCCGEqCaVGwHsCmfPnuWzzz5jzZo1JCUl0atXLyZMmFCVsQkhhBDiNhTllpF1oYDslEKa3OV7zWVMiXNfY+K8sEzH+ZxisgrLWLzhJM/3i8DPxfaq9XbHZXIyKZ2yot3gagHB3arvQGpK7nnIjIXSfGNSvCQPSvMAFfScdXm5Hcsh8zRYO8GA18Hew2whCyGEEEIIIYSoHpVKnJeUlPDf//6Xzz77jG3btuHv78/48eN59NFHCQ4OrqYQhRBCCHEr0hONbVhcfe2xsNRcPT+/lMyCMtRqFWHeDgA42Vgya0Bjlm08xfnsYpZsOMmMvhEEutuZ1tPpDeyLz6Rh6Uk8bRSw9wTPiJo5qOpSlAUbXgT9NVrUaCxBUUClMr6294DcRGNSfdub0Hc+WFjVbLxCCCGEEEIIIapVhRPnkyZN4vvvv6ekpIRhw4bx22+/0a9fP1SXvkQKIYQQolbJSCwAwKOBwzXn5xaX4eVkg5ONBTZXJNadbS2ZOaAxyzfGcC6zkDf+OMmzfcNp5GnczokLeeSX6BigO4qjo4VxUNC6/nkgdpMxaW7nAV6NjdXkNs5g4wTWzuWXvetZKEiHP16C7HjY9x/o/HTdfw+EEEIIIYQQQphUOHG+Z88e5s2bx5gxY3Bzc6vOmIQQQghxmwwGhczzxsS5Z4DjNZcJ9XLk9RHNKdXpr5rnYG3B8/3DeWtzLKfTCli28RTP9A4nwseRXXGZ2OoLaKY+i0plc2e0aWl6L9i5g5MveDe9+fIOnsYE+pbX4Ox2cAuBxoOrP04hhBBCCCGEEDWiwonzo0ePVmccQgghhKhCuWlF6Mr0WFprcPa8ukf5lawtrm7jAmBnZcGzfcN5Z0ssZ9ILUaugqEzHP4k5NC39Bzc7C3APNSab6zoLKwjrU7l1vJtCm7FwcDUc+gJcQ8C7SbWEJ8SdokSr58/oNDRq6N/UR+5eFUIIIYQQtdYtDw4qhBBCiJql0+rJTinCoFMwGBQUgwGDXkExgF5vwNbBCu8Q4yCf2SlFALg3cEClvjoxVaYzoFaBhUZ9w33aWGp4pnc453OKCfGwZ3tsOlq9gbaG49haaYxtWuoyvRZUGlDf+H24rvABkBUPumJwa1i1sQlxB1EUhb3xWXx/IJHcIi0ABgUGNb8DLrwJIYQQQog7kiTOhRBCiFpMURRTRaau1MC+dWeuu6xHoKMpcR7UzJ2YfSnXbdOy43Q6PxxMok+kNyPaNLhhDFYWakI87AHj4KGtnQtpVJKOSmUPgZ1v5bBqj5gNcHoztHoEAtpXfn2VCjpMArVGepwLcR2JWUV8tTeB2FTjgMWONhbkl+j46VASgW52NPN3vskWhLhz5efn079/f06cOMHo0aOZOXMmwcHB5g5LCCGEEMAtllcJIYQQorqUlehIjM5i3y9nOLD+rGm6jYMlvmEuOHvb4epjj7u/Ax6BjngFO+HTyBk3H3vTshoLNeHtffBv7HrNfUQn51OqNWBlUbmPAi0DXJgScgEvJ2vwa20cPLOu0pVB9K+QnwLawlvfjsbictJcUSBxn/FfIeq5glIdX+w5x7xfThCbmo+VhZp72/jzxv0tuSvMA0WBD7bGkZZXYu5QhTCbt956i927d5OXl8f7779PWFgYjz76KDExMeYOTQghhKj3pOJcCCGEqAXKSnSkxOaTHJdDRlIBiuFi4lWloqxEh5WN8U92m35BFd5mw9ae15yuKAonU4yVn5G+lUx8Kwqc3YEKFYTU8UFBz/wNJTlg5wFBd93+9hQFdr9rHCy05cPQdHjF1ivJM8YSOfRyAv7Q52DlABEDwfLGPeqFqG0MBoWtsen8fOg8haU6ANqHuPFguwDc7K0AeKRTEBdyijmTXsi7f53mpUGR2Fhee7wFIe5UOTk5LFu2rNw0nU7H6tWrWbNmDQ8++CAvv/wyLVq0MFOEQgghRP12S4nznJwcPvnkE6Kjo1GpVERGRjJhwgScneU2SyGEEKKyCs9b8NfnMVzZ6MPR3RbfUGd8G7mYkuZVJTGrmKJSHTaWGoLd7W++wpVST0BRJljagV+bKo2rRul1EP0/4/PIocaq8dulUoFnY2Pi/J9vwTXIWJV/PboyY6uYEz+DtgjsPSCoi7EC/tTvoBjg1HpoMgzC+oGF9e3HKEQ1i03N56u9CSRmGcdZaOBqy8MdA2nsU/4inaVGzVM9Q5n/axRJ2cV8tvMsk3s0lMFCRb2yfPlycnJyAHjooYeIiIjgrbfeIicnB0VR+O677/juu++45557mD17Nh06dDBvwEIIIUQ9U+lWLQcOHKBRo0asWLGCrKwsMjIyWLFiBY0aNeLQoUPVEaMQQghxx7jUhqWkUGuaprFSUBQFR3dbwjv60GNUY7o/FE5YO28cXKs+WRqVnAdAuLcjmmsMHHpdBoMx0QvG3uYWVlUeW41J2AWFGWDtBI3urrrthvWB0D6AAjvfhrzkq5dRFDi7E357Fo58ZUyauwSB7cW2OvZe0OkpcPCG0nw4/CWsm2ZMpuu1V29PiFogu7CM/2yLY/HvJ0nMKsLWSsOojoHMGdr0qqT5Ja72VjzVsxFqtYoDZ7P4/XhKDUcthPlkZmaycuVKACwsLHj99deZO3cu586dY/HixXh5eZmWXbduHR07dqRfv35s27bNTBELIYQQ9U+ly6ueffZZ7rnnHj766CMsLIyr63Q6Hn/8caZPny5/yIUQQoh/KSvRkRqfR/Lpy21YmnbzJ7iFBwBWrnq6dQ/FxdOhRuI5mWJMnDf2vfbAoddUnAO73jZWnAM06lX1gdUURYETa43PGw+u+gsAbcdDTgJkxMC2pdB/4eV2K2nRcOgLyIozvrZ1g5YjIbg7qC/WM6jVxjY4QV0gfhsc/8GY5D+4GqJ/gW4zwL1R1cYsxC3S6g1sikrl16MXKNUaUKmge7gnw1v742RjedP1w7wdGdUhkC/3nJPBQkW98uabb5Kfb2ybNmHCBNOAoE5OTsyaNYupU6fy8ccfs3TpUpKSkgDYtGkTmzZt4q677mL27Nn0799f7tIQQgghqtEtVZzPmjXLlDQH4xXymTNncuDAgSoNTgghhKirdFq9aYDPzZ9FcXRLIukJ+SgGY2W5pfXlXr5qC7B3qZk2HDq9gZjUi/3Nr1MFepXUKNjwojFpbmENXaaBR1g1RlnNUo5B3nlju5mwflW/fY0l3PWssYI87zzsXmVM1isKHFxjTJpbWEOLkTB0JTTseTlpfiW1xniBYshb0P5xY5JdW2ysRBeiFjialMOc/53gx4NJlGoNNPJy4P8GN2Fs5+AKJc0v6RnhaRos9MNtZ2SwUHHHS0tL45133gHAysqK2bNnX7WMnZ0d06ZN4/Tp0/znP/+hYcOGpnk7duxg4MCBtG/fnp9//hmDwVBjsQshhBD1SaUrzp2cnEhISKBx48blpicmJuLoWInKNSGEEOIOoxgUVBdbnxj0Csf+TjIN8nllz/LqaL9SUTqDwqDmvsSnFxLgdpNBJxUFov4HR78z9tt28oduz4Fzg5oJtrr4NIfec4y92q3sqmcfdm7GyvDNc+H8Ycg6Y6wSb/0IJOyG5g+ArUvFtqWxgLC+ENLDWMluffHOBEWBPe+BbytjdbpUHYoakpZXwjf7EjmalAOAs60l97drQOeG7rdU/apSqWSwUFGvvPHGGxQWFgIwadIkAgICrrustbU1EydO5NFHH+W7775j4cKFREdHA3Dw4EFGjBhB06ZNefnll3nwwQfLFbgJIYQQ4vZU+q/qyJEjmTBhAm+++SZdunRBpVKxY8cOXnjhBR5++OHqiFEIIYSotcpKdKSeyeNCXA56rYEuI0IBsLKxIKiZO1a2FmZPll/JxlLDkBZ+N1+wtAB2vwsXLo5fEtzNWPVsaVO9AdYElQq8m1b/fjzCjL3K7dwut1bxaWZ83AoLK/AIvfw6+YixlUv8NuMAo80fgIAOkkAX1aZEq+e3o8n8cSIFvUFBrVbRN9KboS39sLW6vST3pcFCF/xrsFAh7jTJycm8++67ANjY2PDSSy9VaD0LCwtGjx7Nww8/zM8//8zChQs5fPgwACdOnGD06NHMmTOHl156iTFjxmBlVX3jkCQkJAAQGBhYbfsQQgghaoNKJ87ffPNNVCoVY8eORafTAWBpacmTTz7J4sWLqzxAIYQQora5MlmeebFn+SUlBVpsHIwtCpp28zdXiLcnMw52LDf21VZbQLtHoVHvOyMhqy2+3G+8JgR3rb5te0QYk+Unf4PcROP/mWswtHgQ/NrcGf9folZQFIV98Vl8fyCJnKIyAJr6O/NwhwB8navu58nV3oqnejXijQ2nOHA2iw3udvRp7FFl2xeiNli8eDElJcZ2RE8++SR+fhW4mH0FtVrNfffdx4gRI/j9999ZuHAhu3btAiAuLo7HH3+cefPmMXPmTCZMmICtbdX9jObk5PDCCy/w8ccfY2try4YNG+jevXuVbV8IIYSobSrd49zKyoq33nqL7Oxsjhw5wuHDh8nKymLFihVYW9eOajohhBCiusQeSDX2LP8rkYxLPcs9bAnv6EOPUY1NSfPaqExnYP/ZLPJKtNdeQFEgZiNsmmNMmjt4Qb/XILTPnZGETT8FP0+Go9+bO5KqYWUHze+He96Bpvca+6Znn4Wtb8DG/4OiLHNHKG7D9th0lm+KMQ3may6JWUUs2XCK/2w7Q05RGR4O1ky5O5Rn+4RVadL8klAvRx7uaKxi/fFQEicumPf4hahKSUlJfPDBB4Cxh/mLL754y9tSqVQMGjSIHTt28Ndff9G7d2/TvMTERKZOnUpISAhLly41DUJ6O37++WeaNGnCxx9/DEBxcTETJkyguLj4trcthBBC1FaVTpxfYmdnR/PmzWnRogV2dtXUH1QIIYSoZeydra5KlncfGU5YO+9a047leuLSC/jg7zjmrjuBoijlZ2pLYNfbcOATMOigQTsYsBjcQswTbHU48TPoSqA4x9yRVC1rB2j5ENyzCiKHGgcn1RaDjYu5IzPSlUFuknGQWV2puaOpE5Jzi/li9zlOnM9l6YZTfLIjnvzrXfCqJgWlOr7Yc455v5wgNjUfS42a4a39WTC8Ga0DXW+pl3lF9Qz3pNvFwUI/2nGW3LJq25UQNWrRokWUlRlP6KlTp+Ll5XXb21SpVPTs2ZPNmzeze/duhgwZYpqXmprKzJkzCQoKYv78+WRnZ1d6+ykpKdx///2MGDGC5OTkcvNOnz7N/Pnzb/sYhBBCiNqqQq1aRowYwerVq3FycmLEiBE3XPann36qksCEEEKI2sgr2IkeoxrX+iT5tVyqXI30cSqf9Mq7ANuWGv9VqaHVaGg8+M6oMr8kKx4uHAZU0OQec0dTPWycjIOPNh4MxdmgvlgfoStDveddHEqqqUWNokBpHhSkGf/1b3t53pbXIOXY5dcO3tBlqrH/u7gmRVH4ak8CeoOCu4MVWYVl7DqdwT+JOTzQrgF3hXpUa9LaYFDYGpvOz4fOU1hqbMvYLtiNke0DcLOvvp7JV1KpVIzuFMT5nGJOp+WzOV3NQ/++2CdEHXPu3DlTtbaDgwPPP/98le+jU6dO/PLLLxw5coRFixbxww8/oCgK2dnZvPrqq7z55ps8/fTTPPvsszdN2iuKwurVq3nuuefIyckxTR88eDDTpk1j6NChlJWVsXTpUkaOHEmrVq2q/HjuJDqdjtLSUuzt7c0dihBCiEqoUMW5s7Oz6QO6s7PzDR9CCCHEnebC6RzOn8rGoDdgYampk0lzgJPJxlu1G/s6lp+x9wNj0tzWDfrMhcghd1bSHCBqrfHfoC7g6GPWUKqdrSu4XTGo4ulNqM7toun5b1BvX2q8iHC7dGUQuxk2vAz/HQc/TTK2h9mx0phIv8Ti4mCylrZgZQ8FqcZWQMd+AIP+9uO4Ax04l010ch4WGhXP94vgpUGRBLjZUViqY/XOs7zxxyku5FRPa4TY1Hzm/xrFl7vPUViqw9/VlhcGRPBkz0Y1ljS/xFKj5uleoWhKS8kq0JGaL3criLrttddeQ6s13jkyffp0PDyqr39/q1at+P7774mKimLs2LFoNMbBe/Pz81m8eDHBwcFMnz6dpKSka65/5swZ+vXrx2OPPWZKmnt4ePDVV1/xyy+/0K9fP2bPng2AXq/n8ccfN41/Jq62ceNGWrZsWeGBYIUQQtQeFUqcf/bZZzg6Opqe3+hRGdu2bWPo0KH4+fmhUqlYu3ZtufmKojB37lz8/PywtbWlZ8+enDhx4prbUhSFgQMHXnM72dnZjBkzxpTcHzNmTLmr5mAcGXzo0KHY29vj4eHBtGnTTLfRCSGEqL8Ug8LJ3ckc2ZzA+VM55g7nlpVo9ZzJKAQg0tfp8ozSfEiPMT7vMxc8I2o+uOqWdwES9hqfNxlm3ljMwb8dSkh3FFSoLhyGDS/C9mWQk3hr24vZCP97CvZ/BFlxl9uv2LmDeyNjO5xL2j4K930M939m7MUe1BUUAxz7Lxz97vaP7Q5TotXzzb4EAAY198XLyYZGng68MqQJD7QLwMpCTUxKPnPXneCnQ0mU6QxVst+cojI+2naGxb+fJDGrCFsrDQ93COTVoU1p7ON08w1UE6uTJ/A8eQir1FROX8g1WxxC3K64uDjTd2VnZ2eee+65Gtlv48aNWbNmDbGxsTzxxBNYWRkvgBUXF/PWW2/RsGFDJk2axJkzZwBjEnz58uU0b96czZs3m7YzevRooqOjGTVqlKmg7sUXX6Rp06YAHDx4kLfeeqtGjqkuOXnyJEOGDKF///5ERUXx3nvvERUVZe6whBBCVMIt9zivCoWFhbRs2ZJVq1Zdc/4bb7zB8uXLWbVqFfv378fHx4e+fftec3CTlStXXve21VGjRnHkyBE2bNjAhg0bOHLkCGPGjDHN1+v1DB48mMLCQnbs2MG3337Ljz/+yIwZM6rmQIUQQtRZKfF5FOeVYWljgV+Yi7nDuWWxqQUYDAoeDtZ4OFxRMZ96AlDAuQE4epstvmoV9T9AAf924Bpk7mhqnqM3hg6TORowDiWwM6CCxH2w/gXY9Q7oK1klaNAaL7jYe0CbsTB4OTz4BQx/z3jxxfKKljD27mDtaLyDwcoeuk6DLtPA0dfYUkaUs+7IBXKLtHg6WjOwma9pukatYkAzHxYMb0bLABf0BoXfjibz6rrjHD9/6wllrd7A+mPJvPzzMfacyUSlgu7hniwa0Zw+TbzRqM1350nxiRNkfvoJwfoC9I4OxGWX3HwlIWqpBQsWoNcb77J57rnncHV1rdH9h4SE8MEHH3DmzBmmT5+Ora3x97RWq+Wjjz4iPDycMWPG0KVLF2bMmEFRUREAAQEBrF+/ni+//PKqCnkrKys++ugj03fwV155xZSAr++ysrJ45plnaN68Ob/99ptpert27UzngRBCiLqhQj3OW7duXeFeiocOHarwzgcOHMjAgQOvOU9RFFauXMns2bNNfdXXrFmDt7c3X3/9NU888YRp2X/++Yfly5ezf/9+fH19y20nOjqaDRs2sGfPHjp27AjARx99ROfOnTl16hQRERFs3LiRqKgoEhMT8fPzA2DZsmWMHz+ehQsX4uRkvkobIYQQ5hV/JB2AoKZuaCzNer35tkQnG/ubX9WmJfmo8V+fFjUcUQ0pK4RzO43Pmw43ayjmVmLlhqHzI2gKk40V34n7jIPCam7wcTD9FET/AoGdIbircVqju43tYAI6glpT+UCCuxq3p77i5+nU78ZqdJv6+5nrfE4xm6JTARjVMRAri6t/33g4WDP17lAOJeTw9d4E0vJKWbEphg4hbjzUPhBnO8sK7+9YUi5f70sgLc+YkG7oac+ojkGEeJi//25JTAyZH3wIOj0RkX6sK3bmdHqhucMS4pacOnWKL774AgBXV1eeeeYZs8Xi7+/PihUreOmll1i5ciWrVq0iPz8fvV7Pl19+aVpOpVLx1FNP8frrr5vuPL+Wzp07M2XKFN555x2Ki4t54okn2LhxY7WOw1CbabVaPvjgA1599dVyA7E2aNCAJUuW8PDDD9fb90YIIeqqCiXOhw8fXs1hXC0+Pp6UlBT69etnmmZtbU2PHj3YtWuXKXFeVFTEww8/zKpVq/Dxubpn6e7du3F2djYlzcE4aIqzszO7du0iIiKC3bt306xZM1PSHKB///6UlpZy8OBBevXqVY1HKoQQorbKSS0iO6UQlVpFUPPq60VaE6IvDQx6ZZsWRYGUf4zPfe/QxLmVPQxeAUn7ZUDKS1wCodsMY69ziyvuPijMhKifoclwyDoD0esgI/bivHRjf3iVylhRHtTl9mK4Mml+diccXA0n1kKnyeDX+va2XQcpisKXe85hMCi0DnShRQOX6y6rUqloG+RKUz8nfj58nj+jU9kXn8X+s1nYWGqws9Jga6nBztoCO0sNtlYa7KwssLO6OM9Kw6FzORxNygHAydaSB9o2oHMj91qR0Ck7e5aM995H0Wqxad6MFqMfgA//JjWvhPwSLY42Fb84IERtMG/ePAwGY1ulF154oVaMC+bl5cWiRYt44YUXWLVqFStXriQrKwswtnf5+OOP6dq1a4W2tXDhQtauXUtiYiKbN2/m888/Z9y4cdUZfq30+++/89xzz3Hy5EnTNDs7O2bNmsXzzz+PnZ2dGaMTQghxqyqUOH/11VerO46rpKSkAODtXf62cW9vb86dO2d6/eyzz9KlSxeGDbt2z9KUlJRrjhju5eVl2kdKSspV+3F1dcXKysq0zLWUlpZSWnp5oKK8PGNSQqvVmgZ+uZNdOsb6cKzCPOQcE+Z2+lAqBkXBr6EzGqvqORdr6jyf3C2YU6n5hHvaXt5XfjKa/DRQW6B3DYU76Wft0sCTag1Yu0CjvnfW8VXSNc8zxwaXZgKgPvpfVHF/wqk/Li+jtkAJvgtDxGCoroHf7LxQO/iiyjsPWxahhPbF0HJU+aT+HW5vfBYnk3Ox1Ki5v7VvhX4faID7W/vSPtCZr/YlcjazkMJSA4WlFTvPNSoVdzf2YkhzH2ytNLViYD/thQtkvvU2huIirMPDcX70UbRaLZ5F2ZQllXHyfENaBbqYO0xxh6nOv8MnTpzg22+/BYyDa06ePLlWfa51cHDgxRdfZMqUKXz99dcYDAbGjx+PjY1NheO0sbHhnXfeMRXbPfvss/Tu3fuq79d3qqioKGbNmsUff/xRbvojjzzCggUL8Pf3B2rH95nbPddrwzEIIURNq1Di/FoOHjxIdHQ0KpWKJk2a0Lp19VQH/bvyRVEU07R169axZcsWDh8+XKlt/Hs7FV3m315//XXmzZt31fSNGzfWqyvKmzZtMncI4g4n55gwB32piuzj1qCoKHNPIGn9P9W6v5o6z7dfLoTCO/cIwRlp5NkGEL1xS43svyZY6vIJTfudfJsGJLndZmX0HeZG55ljsYEG+VY4FSehU9uQ6tySVKeWaNMdIP0foPp+BlSGbgSWbscn9zCkfUXxgd857TWYImvPattnbVGqh+/PqCnWQ3sPhb3bLlR6Gy0ViHAybqvMYHwYn6so01/52vivjQZauyvYpybzV2r1/m6rDOvz53FLTEDr6kZmUBDKpk2g1xOSFMtRWz/+98ufXAixvfmGhLgF1fF3+I033kBRFACGDBnCtm3bqnwfVaVBA+PF1C1bbu3zQLdu3di+fTvZ2dmMGjXqjh4vrKioiLi4OHbt2sUff/xhuqMAjBX7jz32GOHh4fzzzz/880/t+R17ya2e65d63wshRH1S6cR5WloaDz30EH///TcuLi4oikJubi69evXi22+/xdOzar7gXGq7kpKSUq5veVpamunq9ZYtW4iLi8PFxaXcuvfddx/dunXj77//xsfHh9TU1Ku2n56ebtqOj48Pe/fuLTc/OzsbrVZ7wyvlL730UrkR0fPy8ggICKBfv371oi+6Vqtl06ZN9O3bF0tLuW1WVD05x4Q55WeWEK1JARV0GBpcbfsx53mu3hGNSu2FR4t7CYkcVKP7ri6qC4dQ71sLjmVglUKL3t2Mg1PWcxU+z5THoSgTrB3wtrCpuQABuAdSjqLZ9yEUZxOk/wtD88koAZ1qOI6a9e3+JBxz0wh1smHm4MZYauruWApVoax3Hyx8vFFfLELRarUkbzmElZUVGhtXBg2628wRijtNdf0d/ueff9i1axdgvGt65cqVd3RxVfv27WnRogWZmZls376d5557jsGD6/4g0GVlZRw/fpz9+/ebHidPnjRdELkkMDCQRYsW8cADD9SKtlfXcrvn+qU77IUQoj6pdOJ86tSp5OXlceLECSIjIwHj7Unjxo1j2rRpfPPNN1USWEhICD4+PmzatMlUzV5WVsbWrVtZsmQJAC+++CKPP/54ufWaN2/OihUrGDp0KGAcsCQ3N5d9+/bRoUMHAPbu3Utubi5dunQxLbNw4UKSk5NNSfqNGzdibW1N27ZtrxujtbU11tZX30ZsaWlZr5J89e14Rc2Tc0yYg5uPJV3vc0SvNdTIoKDVeZ5/vvssXo42dAvzwN764p9+vQ7So0CtRt2gDdT1nzG9Dv75Bk7+anzt3gjumo7awc28cdUyFTrPrHxvPL86BbQFzzdh9ypIPora2r7un5tXUhTQlYKl8aJEYlYRW2MzUKvUjOkcjJ1N/WlPc4k+JwdDaRmW3sbWipYR4Vct4+lmAwWQkFEIak29v7ggqkdV/x1+7bXXTM9feumlWtHbvDr5+fmxYsUKxo4dCxjzBr17977h4KK1jaIonD59mn379pkehw8fLtee9d/s7e1NBXW2tnXjjphbPdfl+5gQoj6qdOJ8w4YNbN682ZQ0B2jSpAnvvvtuuYE8K6KgoIDTp0+bXsfHx3PkyBHc3NwIDAxk+vTpLFq0iLCwMMLCwli0aBF2dnaMGjUKMFaKX2tA0MDAQEJCQgCIjIxkwIABTJw4kQ8//BCASZMmMWTIECIiIgDo168fTZo0YcyYMSxdupSsrCyef/55Jk6cWC8qx4UQQlxfTSTNq1NOURlbT6WjUsFdYVcMcJoZa0zgWTuBa7DZ4qsSBWmw8y3IvPiZInwAtH4ENPIFr06ycYIeL0JaFPg0uzxdUYyDk9ZlhRmwcTZ0mYbi3ZQv95xDUaBdsBtN/e7spNq16AsKSX/7bfQFBXg98wyWF3sB/5udlysO+TqKCos4l1lIqFfdScSJ+mn9+vX873//A4wJ5SeeeMLMEdWMRx55hC+//JKNGzeSlJTEyy+/zDvvvGPusK4rJSWF/fv3m5Lk+/fvJzs7+4brWFpa0rJlSzp06ED79u0ZOHBgvennLoQQ9VGlE+cGg+GaVxotLS3L9faqiAMHDtCrVy/T60ttT8aNG8fq1auZOXMmxcXFPPXUU2RnZ9OxY0c2btxY6avWX331FdOmTTMl9u+55x5WrVplmq/RaPjtt9946qmn6Nq1K7a2towaNYo333yzUvsRQghR9ykGhfh/MvBv7Iq17S0PBVJrnEzJB6CBqx0O1lccT/JR478+zep2MlJXBpvmQHE2WNlDp6egQTtzRyVul1pdPmlekAbb3oQOk8Aj1Hxx3a7iLPAIg0Nr2Bn6AqfTCrC2VDOyfYC5I6txhuJiMt55B+2FZDTOzte/s6AwHZW7LUGx2USVWRJzNl0S56JWS0tL49FHHzW9njdvHjY2Nd36yjxUKhUffPABzZo1o6ioiHfffZeHH37YdKe3ORUUFHDw4MFy1eQJCQk3XS8sLIyOHTvSoUMHOnToQMuWLevN/6cQQohbSJzffffdPPPMM3zzzTf4+fkBcP78edPo2ZXRs2fPq3qDXUmlUjF37lzmzp1b4W1ea3tubm58+eWXN1wvMDCQX3/9tcL7EUIIcWdKic8jetcF4v9J5+6xkajUdTipDEQnG/tRNvH91x1UKRcT574taziiKmZhBS0ehLi/oOszYO9x83VE3XPkK8g5B5tfhXaPQWjlPnOajUEPR78D76bGnzUnP0iNorCkjP9eOAwWbtzT0g83eytzR1qjDGVlZLz3HmXnzqF2cMBz+jNYenldvWDKMTR/vU7TtGyynfoTVQQnY5IY1KFhzQctRAUoisJjjz1GWloaAIMHD2bChAlmjqpmhYSE8Nprr/Hcc8+hKAqPP/44hw8fvqrFaWFhIbGxscTExJj+PX36NI6OjrRr14727dvTvn17U86hMrRaLceOHStXSR4VFXXTQj9vb29TgrxDhw60a9cONzdp+SaEEPVZpRPnq1atYtiwYQQHBxMQEIBKpSIhIYHmzZvfNDkthBBC1Hbx/6QD0KCxa51PmgOcTDZWnDf2vaJCszQfMuOMz31amCGq25R7HnQlxj7mAA17QUhPY5WyuDN1eAIMOkg6APv+A1lnoO342t2OpyjL2EIo/aTxws7Qt4wD1TYZxk9bDlJQeB7fcB/6RNavW/zLEhPJ+e47Sk/HobK1wXPaVCx9r9NXP3Yj6LVY6osI89TAOTidXoCiKLV28D1Rv73//vv89ttvAHh5efHpp5/Wy3P10thn+/fvJzo6mqlTpxIREUFMTIwpUX7+/Pnrrv/HH3+Ynvv7+5uS6O3bt6ddu3a4urqa5iuKQlxc3FV9yUtKSm4Yo729Pe3atSuXKL+U3xBCCCEuqXTiPCAggEOHDrFp0ybTaNJNmjShT58+1RGfEEIIUWNyUovITi5EpVYR1LzuVy6n55eSUVCKSqUi3PuKxHnqCUAB5wZgV8cqqc78Dfs/MSYgBy4x/qtS1e12M+LmrOyg2/Nw4mc4+j2c3gzZZ+Gu58De3dzRXS35KOx6B0rzwMLGWCVvZQdAvHsPthZmgr6MR3wSsdDU8bs+KqE0Lo60pcZWiCorKzynTMEqMPD6K3SZBuf2ANC4rTsOTqGUoiI1rxQfZ2mVIGqXqKgoZsyYYXq9evVqvK51J0U9oNFo+Oijj2jXrh06nY6PPvrolrd1/vx5zp8/z9q1a03TQkNDadeuHdnZ2ezfv5+srKybxtOiRYtySfLIyEg0Gs0txyWEEKJ+qFDi3M3NjZiYGDw8PHjsscd466236Nu3L3379q3u+IQQQogac+Zitbl/uCs29rW4krWCTqYY27Q09LTHxvKKL4em/uZ1qNpcWwL7P4az242vHX2NbTDEHUmrN2Cp+dcdBCoVNBsBbiHGpHTmadjwIvR51XgRqDYwGOD4D3D8J0ABlyC461lwMlZUK4rClweSUZz86KjdR+ML+6Gsl7E//x1K0WpRXexfbtWwIZYBAVj6eOM0ZCiW3tdIKhbngI2z8f9bY4mh5WjYtALbsgRCvFtzOrWA2LR8SZyLWqW0tJRRo0aZqpynTp3KwIEDzRyVebVs2ZKZM2eyaNGiq+Z5enoSFhZGeHh4uUejRo3IyMhg//79pkE7Dxw4QH5+frn1T58+zenTp6+770aNGpVLkrdu3RpbW9sqP0YhhBB3vgolzsvKysjLy8PDw4M1a9awZMmSSg/QKYQQQtRmxfllJJ/OBSCkZd2vNgfILdZioVHR2OeKv9mKAin/GJ/71pHEeVa8seVFfjKgMvY0bzJcWrPcgZKyi/jfkQscTsimVYALD3UIxMOhfF9c/FpD/9dh+5ugtgT7i8lXXRn8/bqxstvS3piMtrIHSzvjNKcGlwcWVRTQFoOlbdXdraArg61LIPW48XVoH2gzztiHHzAYFH49lszZjEJsnLx40KEIigog6n/QalTVxFCL6HNzydvwB8VH/8FnzhzU1taoVCq8X3geldV1errnp8CmVyGoC7QZCyoVikc4AKqMGMJCHDidWsDptAK6hXnW4NEIcWOzZ8/mn3+Mf1ubNm3KkiVLzBxR7TBv3jy8vb3JyMgwJcfDwsLKtVr5t8DAQAIDA7nvvvsAMBgMxMTElEumHzlyhNLSUsCYhL8ySd6+fXvc3WvhnUhCCCHqpAolzjt37szw4cNp27YtiqIwbdq0616x/fTTT6s0QCGEEKImxB/NAEXB3d8BJ4/aV5WkKAoxqQXEZxTSJsgFL8ebV1sOaeFHvyY+6K4cDCs/GQozQG0BnpHVGHEVUBSI3QSH1hj7W9u6Qddp4FXL4xaVlpxbzLojF9h/NotL47wfTsjh+Pk8hrT0pX9Tn/IV6I7e0O810BaZEtOUFUBa1PV3EtL9cuJcr4UfHgVUFxPsl5LtdsbXXk0g4mK1qKLA2R3/WsbBmJC3sL6ceNdYgp07aKygw0Tj/i46cSGX7/cnkpRdDMCw1v64OD8IR74Ej4gqeAdrD31BIfmbNlHw118oZWUAFB8+gn2njgDXT5oXZsCfC6Akx3jxQVtsfK9dgzGoNFCaT4PEo5TG5ROVnwFdQ2roiIS4sc2bN7Ns2TIArKys+Prrr6W6+SILCwumTZt2W9tQq9U0btyYxo0bM2bMGMBY2Hfq1CkcHR0JCgqSvuRCCCGqTYUS519++SUrVqwgLs44kFhubu5NB9sQQggh6hq1Rk1Iq9pVxZhXomXX6Qy2xWaQmmv829vI096UOI9JzSc+o5BGnvYEuNlhbVG+X6eVhRorrkg4phwz/usZAZZ1oNVBylFj0tyvDXR6EmyczB2RqEJpeSWs++cCe85kmhLmbYNd6drIgz9OpHAqJZ+fD51n5+kMRncMopm/8+WVLayNj0ss7Yw9sbVFUFZ4+d+yAigrAtfgy8uWFVx8olycX0A5Flf8bOi1sHvVtQ9ApYGgztBlqjGB3u4xaDIMnP0BSMkt4bv9iRxNygHA1krD8Fb+9I70ArzBrxWo74weu4aSEgq2bCF/82YMRcYLBFYhITgPuwebxo1vvHJxNmxZAEUZxjZMvWabesKjsSTNqQVeTdoTnKjFUFREcqaG/BItjjZ1v6WWqNsyMzMZO3as6fWSJUto0aKO3M1Vh1lZWdG8eXNzhyGEEKIeqFDi3Nvbm8WLFwMQEhLCF198Ibc/CSGEuKM06epHozZeWNmYP4llMChEJeexNSadI4k5GAzGjKK1pZpQL0cC3OxMy+4/m8WW6DQAVCoVDVxtaehpT6CbHa0CXHCx+1d1Z13ob64olwf87DgZfFsa215IRdkdI6OglF/+ucDO05koFzPmrQJcGN7a33R+t2jgzL74LL47kEhaXikrNsXQJsiVh9oH4P7v9i1gvBAU3LViAdi6woNfGBPmpgT7pWR7kbGi/RKDFnyaG6ebli8CRW98qK64MGVpA87+FJTqWHfkAn+dSsNgUFCpVNzd2It7WvnhYH3Fx2+V+X/fVAVDYSHJc+dhuNiH2NLf35gwb9785pWgpfmwZaGxTYu9B9z9Cti6lFvknEcvmjYfhJvbebz+/o30IjWxqQW0Cbp+uwchqpuiKEycOJHk5GQA+vXrd9vV1UIIIYSoXSqUOL/SvHnzcHBwuGp6WVkZ3377bbkr7kIIIURdYm1b6T+L1SIuvYAVm2JMrxt62tMtzJMOIW7lB/kEQtztaRXgQnxGIbnFWhKzikjMKgJgU1QqozoG0tTvYpWuXgepFyvOfVvWyLFUiqJA9C+QkwCdnzYmyq0dIEwGI68tFEUhKbuY6OQ8opPzOZNRgJ2VBg8Ha7wcrfG89HCwwdPRGlur8udrVmEZvx29wPbYDPQXLwg1b+DMsFb+hHiUHyBTpVLRsaE7LRq4sO6f82yKSuPQuWyOJeUytKUf/Zp6Xz2AaEWpVMYWLxZugNuNl7Wyh7v/799vBOhKjUn0KxLDOr2Bv0+l879/LlBUqgOgRQMXHmzfAF/n67Ru0GshdiNkn4POT93a8ZiBoiimpLja3h7rsDC0iYk43zMU23btKtY6oawI/nodchONFzPufgXsr1+cY9mgAcGqYtL0tsTEJkniXJjVJ598ws8//wyAu7s7q1evRi1jbwghhBB3lEpnCB599FEGDBiAl5dXuen5+fk8+uijkjgXQghRp+SkFqFSq3D2NE8/Ur1B4Ww+bD+dwd2RvgCEejnQ0NOeEA8HuoV5lKsw/7cuoR50CfVAURSyi7TEZxQQn1FEfEYBJVpD+WRdZqwx2WftVL5tRW1Qkgu734PkI8bXId3rzuCld7iMglKik/OIupDHyZR88oq15eYXlOhIyyvlWt3FHWws8HQwJtMtNGr2nsk0Jcyb+DkxrJU/oV5XF2RcydZKw8j2gXQN9eCrvQnEpOTz06EkdsZlMKpDYPn2LTVFpTJWl19sd6QoCkeTcvnuQKKppZK/qy0j2wdcvnB1PYXpcPhLUAzQ6G7wuklbEzNTDAaK9h8gb8PveE6ZgsXFu1BdR41CbWONyqISXy/SoyHrjLFn/N3/B44+11+2NB9VzhlCPWzZlwmnzqQA0qpBmEdMTAzPPPOM6fUnn3yCr6+vGSMSQgghRHWodOL8yuqSKyUlJeHsbIYvLkIIIcRtiNpxgeyUQpr1bEBQ05prQ5aWV8L22Ax2xKYRc15NDBe4K8wbKws1KpWKlwdFVmqwK5VKhZu9FW72brQNuk4FralNS7Pa1fYk9QTsesfY51hjCW3HG1tjCLMoKNVdrCg3PtLySsvNt9SoCfdxpImvI+Hejmj1ChkFpaTll5CeX2p65JfoKLj4iM8oNK0f7uPI8Fb+RPg4ViquBq52zOwfwZ4zWfz3YoJ6xaYYWge6EOhuf/MNVKPY1HyiLuQB4GhjwfDW/nQL80SjrsDPmZOfMWF+erNxsNC+C2rXz+dFiqJQ8s8/5K77Be2FCwDkb9qM60MjAdA43ML/gX9b6PqMsTWOc4MbLqr+awHkXyDCsxtkwtm0fLR6w63fdSDELSorK2PUqFEUFRnv7nriiScYNmyYmaMSQgghRHWocOK8devWqFQqVCoVvXv3xuKKahK9Xk98fDwDBgyoliCFEEKI6pCdUkh2SiEqtQrv4OofdFKrN3DoXDbbYzOITjYm2QyKARsN3BXmjlZvwMrCmASqTNK8wlIuJs5rS5sWgwFO/ATHfgAUcPI3JtFcg8wdWb0Un1HIxhMpHDiXbeqrD8ZzsaGnPZG+jjTxdaahp/1VycoIrk6Cl2j1pOeXknYxkZ5XrKWZvzORvo63fH6rVCo6N3KnZYAz645cYHN0GocTcjickHNL26tKGrWKvk28GdzCFzurStamNLsP4rdBRiwk7YeADtUT5C1QFIXSkyfJ/d86ys6e/X/2zjo8jut82/cso1ZaMVloSWZmZjvoMDZNmqZJ2v7K+JXSJmnTJm3apJg0DTQNO2wHzcwMki1mWNGCluf7Y2zJikmSBYZzX9dcC0NnFmZ3nvOc5wVAZTJiXbAAy9y53d9gaxW0NUP8UOVx2pSurRedA84qkqPaMMtaPG4PpQ432XHd64ARCM6Xhx56iJ07dwKQm5vLH//4xwFukUAgEAgEgr6iy//qly5dCsCePXtYtGhRp5xznU5Heno6N9xwQ683UCAQCAR9R9AfQq1V9Y1I28u0ufwYzNpea6vXHWDXp2UAJOdEYTBre2W7Z+PDfVV8uFcpIiZJMDTJxtSMSKoO1HD12GS02j7MWPc5wVGo3L9QCoNu/iuUblTuZ85RnObHoy8E/UM4LLOnoplPD9ZytNbZ/nxSpJGhSREMSYwgN956SlZ5VzBo1aTaTWeNGuopJp2GWycq8S0bjzXgD4V7fR/dwaBVMzs3ljhrDz+/JjvkXaV0JO15RXFiqwa+cKgsyzT84x949ym1ESS9HuvcOVjnz0dl7qbD3FUPB5ZB8VrleK/6C6i7fs6TY3KgZA0GfSMZWhOHDQaOVrcK4VzQb8iyzMqVK3nssccA0Gq1vPLKK5i7+10QCAQCgUBw0dDlf6u/+tWvCIVCpKWlsWjRIpHhJhAIBJcAhzdVU1faStLgKJJzIomIGZic73NxdEctBVtrsCdZmHBVOhrt+QlKQX+I7R8W43X6MUfqGTK193/TfMEQO0qaSLAZyIpVOpunZMaw8ZiD6dkxTB8cQ4xFTyAQoPZgr+/+VGoPArISh2A6RzHE/iJ7PlTthvFfgYwZA92aywpvIMTGYw18fri2PYpFpZKYlGFn4dAEBkX3vtjdF6TaTdw6cdBAN6N3GHK1UiTUWQ2Fq2Hw/IFuEZIkoUtJwXvoEJaZM4lYvBh1RDdH57Q1wcF34NhKCCsFU7ENAr8LjJFd3owckwOAyl3G2Nu/TuHuGo41eLrXFoGgi9TV1XHw4MFO06FDh3A4HO3LPPLII4wdO3YAWykQCAQCgaCv6Za1Ta1W88ADD3D48OG+ao9AIBAI+glZlmkod+J1BSjaXUfR7jqs0UaSBkeSnBOJ0aob6CYCULSnnoKtNQA013lwOrxEJfTc3RUOhdn5cSmtDW3ojBomXJWBzth7Tu9Sh5t1RxvYUuTA6w8xNi2Kb8zJBiDBZuDxG0cOjMO/Pd98AN3moSA0l0J0lvI4fihc+1fQCbdef9Ho9rPuWA1rC+pp84cAMOk1zM6JZW5eHFHmC+N7f1miMymRLbteVJzZmbO75cjuDQK1tbR+8AHmGTMw5OYCYJ0/H/P06Wjs3exw8znh8AeQvwJCxwvKxg+DUbdBzODuN84SDwYbeFvI1jcDcKzedcb6SwJBVykuLmbFihV8/PHHHD58mIMHD9LQ0HDWdWbPns33v//9fmqhQCAQCASCgaLb/8ZHjBhBUVERGRkZfdEegUAgEPQhbS4/xXsayJ2SgFqtYuZtudSVOqksaKKupBWno418Rxv5W6pJyolizIKBdXKWHXRweKNShG7QsGgSs23nJZoDOCrdNJQ7UWtUTLgyA7NNf97t9PiDbC1uZF1BPWWODgdkrFXf7jY/wYAIPLIMNXuV+4kDJJy76mDjX6ClHBY/phREBCGa9xOlDg+rqiQ+ePcgMspnMC7CwMKh8UzJisZwnqM4BL3E4AXQXAZ5V/SraB50OGhdvgL35s0gywSbmjD88IcAqEwmVKYejEBoqYBD7yn3o7MVwTxheM8bKUkQmwvl20gLFKJRp+B0tlHb6iPB1rcRTwUFBWi1WtLT04VIfwlx8OBBfve73/Hqq68SDp877ikxMZFhw4YxdepUfvCDH6BWi/OmQCAQCASXOt3+R/7oo4/ygx/8gIcffphx48adkukW0d3hmwKBQCDoF0LBMDtXlNJS7yHgDzFqbipqjYrELBuJWTb83iA1RS1UFjTTWOnCaO3I/A6HwtSWOIlLs6LWqM6yl96jsqCJ/WsrAcgcE0felIROgoW72YfepEHTzezl2EFWxixMQ61VERnfDTGochdsexZicyBrHiSMUIQc4E+fFlDc4AaUAoFj06KYOTj2vIog9irOanA3gEoDsUP6f/9lW2HrPyHgAa1JacsJ4VzQZ4TDMnsrmvn0UC1Hqluoa5WIM8gMSYxg4bAERqXYLozPp6ADtRYmP9Bvuwu1tND68Se41q+DoDICwTByBLZrrun+xoJ+aCyEuOPnmLghkLtEOVcmjW0/X54XMYpwrqo8QMyBGopDegoqM0iw9U2EpCzLPPTQQ/zmN78BYNCgQcyePZs5c+Ywe/Zs0tPT+2S/FzsrVqzgj3/8I2azmdTU1FOm5ORkdLqBG92yc+dOHn30Ud55553Tzk9ISGDYsGHt09ChQxk2bBhRUVH93FKBQCAQCAQDTbeF88WLFwNwzTXXdLrYOjFMMhQK9V7rBAKBQNAryLLM/jUVtNR70Bo0DB4ff8oyOoOGQUOjGTQ0mjaXv9M5vr7Mxa6PS9Do1CRk2UjOiSI6yYyk6hvRzesOsG9VBcgyacOjTxHNnY1etrxXiDlCz8SrM7oknsthub29SYMju9egcAh2vgBtjQRLNtN0cC1RccloBs+DzNlMyozGGwwxc3AsU7KisRr6vtBotzgR0xKb27/FN4N+2P0SHP1MeRydDdO+A5bY/mvDZYg3EGJTYQOfHerIL1dLEoMjZL55RR7Z8bYBbqGgy/icoD978cuTo0rkQIDaxx9HY49GHW1HEx2DJiYaTXQ06pgYVPqOETbOVatpefddZL8fAH1uLrZrr0Wf2c1RpaEgFK5SCpv63XDNU2A8LjCOu7t72zoXKeNBZ0IVO4SM1a9SLOs4kl/OzKG9L5zLssyPfvQjnnjiifbnysrKeOmll3jppZcASEtLaxfRZ8+eTVpaWq+342KjubmZ22+/nZaWlrMuFx8ff1pR/cSUmJiIRtO7oy7Wr1/Po48+yieffNLp+ejoaBYuXMh9993HyJEjiY6O7tX9CgQCgUAguHjp9r+R1atX90U7BAKBQNCHFO9toDK/CSSJcYvTMEWc3elltHSeHwqGMFi0eF0BKg43UnG4Eb1ZezwPPYqIGEOvOlcNZi2jFwyivszJsJnJp2w7FAgTDsk01bjZ9kHxOcXzutJW8rfUMP7K9FOOrUuUbiLYUk2xU8W2cB6DPXtRq6qwt70O+99kbuIY5o+fi5QUC6oLcOh2zX7ltr/zzTc8CVW7lPtDr4URN/d7ZvPlRKPbz6ojdawtqMfjU4owmvQaZuXEMjM7is1rqkmzXxxFPy97QkHY+TwUr4XZ/0+pB/AFwm43ze++C8Eg9i9/GYBgYxOBsnICZeWn3axl1kyibrsNUCJYZL8fXUYGtmuvwZCX1702hsNQsg72vwXueuU5Uwy46juE897GmgDWBCRgcIqdVSVhjpadPYu6J4TDYb71rW/xt7/9rf25KVOmsHv3brxeb/tzpaWlvPDCC7zwwgsAZGRktIvoc+bMITU1tdfbdqHz9NNPn1M0B6itraW2tpYdO3acdr5KpSIxMfG0onpKSgqpqakkJCSgUp19FJwsy3z66ac8+uijrF+/vtO8pKQkfvCDH3DPPfewdu1apk+fjlZ7gXV8CwQCgUAgGFC6ffU6a9asvmiHQCAQCPqI+nInhzdVAzB0ehLRyZZzrHEqSYOjSMyOpLHKTWVBM9WFzfjcAYr31FO8p56Zt+VitZ+/k/lk5+SJCJnTERlvYtLVmWz9oOic4nlzrYddH5cSCoYp3tvA0GndjAiRZTj4DpXNbXyumsUOy2yKU68jKr4Ku3Mr1OejrtoJVTvBaFcK+mXNvXBc1aEg1B4Xzvsz37y1+rhoLsHsH0PSmP7b92VGqcPNpwdr2VbSSDgsAxAXoWfh0IT2/PJAIDDArRR0C7UGPA6lqObqR2HcPTB4PqCcJz1bt9G8bBlhpxMA66JFaBMSUNsiiPnG1wk2NBBqbCTY4CDoaCDU4CDs8aCydLjXTRMnoI6woh8ypHsdn7IM5Vth3xvQqsRpYYiEYddB9jwlbqYfyM1NhZJSqpvcOL2BXhvpEwqFuP/++3nuuecApS7Fv/71L+677z58Ph/btm1jzZo1rF69mk2bNuHz+drXLS4upri4mOeffx6AzMzMTtEuKSkpvdLGC5XW1laefPJJANRqNRs2bACgvLy8faqoqGi/X11djSzLp91WOBymsrKSyspKtmzZctplNBoNycnJZxTWy8rK+O1vf3uKOJ+ens5PfvIT7r77bvR6vTg/CgQCgUAgOCM9sn01Nzfz3HPPcfjwYSRJYujQoXzlK1/BZhNDfwUCgeBCwt3iY/cnpSDLpOTZSR/R8+HHkiQRnWwhOtnCsJlJ1B8vKtrmCnQSzY/uqEVn0JCYbUNn6PrPTHOthwNrKxm3JA2j9dyu8NOJ5xOuzkB7knjubvGxfXkxoWCYmFQreZMTunfQAGVb8DeWU+tVsy96Mt9dkMPwZBswBrhSKYBXuAqK1kJboxJXcPAdJdc3ex4kjx9Yl7XjKAR9StxDVD8W9tYaYNj14G2+qERzWZZpC4RweoM4vQFavcH2+51vg2TFmrljUhqqPoosOlc795Qr+eUFNc7253MSrCwcGs+olMgBaZegF5n2Hdjyd0Wk3v4sNBUTSF5C0xtv4cvPB0CTmEDUbbehTVDObSqDAeOIEafdXNjj6fRYUqkwDD3VyX5O2ppg09MQDirFfYdeCzmLQXP+hZa7hLsByrdh1zUSF26j3qPmaK2LsWnn73IPBoPcfffd/O9//wMU1/MLL7zAl770JQD0ej0zZsxgxowZ/OIXv8Dr9bJ161bWrFnDmjVr2Lx5cychvaioiKKiIv7zn/8AkJ2d3e5Inz17NsnJyefd5guJv/3tbzQ1NQFw5513MnnyZID22y8SCASoqqo6rah+Yqqrqzvj/oLBIKWlpZSWlnapfXl5efz0pz/ltttuE85ygUAgEAgEXaLbV/I7duxg0aJFGI1GJk6ciCzL/OlPf+LRRx/l008/ZezYsX3RToFAIBD0AK9LcVFFxpsYPvvUyJOeolarSMi0kZBp6+QWCwZCFO6sIxQMc3B9JbGDrCTnRhGfHnHWoqKtjja2fVhMwBskf2sNo+cP6lI7IuNNTLomk63vK+L59pPEc783yPYPi/G3BYmIMTJ2cRoqdTcLm8oyHHybBpePPcapZCbHHhfNT8KWAmPvglG3QcUOKFypRKPU7FMmfQRkzFRE9IEoiHki3/ykYqb9gjEKRt3Sf/s7T4KhME+tPMqRGieh8OkdkF+kvNGDWa/h+rH95yL1BkJsLnTw6aGa9vxylUpiYrqdBUPjSY8xn2MLgosGrQGmfxcOvYu861Va336T1qLXIDIDSW8i4sorsc6fh9TFHGiV6TxieprLIPL4edlkhyHXKOeTvCsV8bw/aS6DXS+iNcaSLqVTFzJScLTivIVzv9/P7bffzrJlywDFzfzKK69w0003nXEdg8HArFmzmDVrFr/61a/wer1s2bKl3ZG+ZcsW/Mcz5AGOHTvGsWPH+Pe//w3A4MGDOznSExP7pshpf+ByufjjH/8IKB0O/+///b9zrqPVaklLSztrNrzP56OiouK0ovoJsd3hcJx1P6NHj+ZnP/sZ1113HWr1BRinJhAIBAKB4IKl28L5d7/7Xa655hqeffbZ9oItwWCQr371q3znO99h3bp1vd5IgUAgEPSM6GQL024ajFqjQt1d0biLdBLjZciZmEBlQROtDW3UlbRSV9KKWqsmMcvGoGF2ohI6iyzuZh9b3y8i4A0qAv/M7jnwIuM6xPOwLIMMoWCYHctLcDf7MFh1TLiqsxO9y1TsINRYSo0H9tin8sCwszjW1VpIm6JMzlooWg1FaxR35pEPlSk2TxHQUyeDpgdZ6z2h5rhwnjiqf/Z3kbKtuJGDVa3tj/VaFREGLVaDBoteS4RRg9WgxaLXEGHQ0NwWYNnOCpbvqyYlysTEDHuftq/J7WflF/LLjTo1s3PjmJsXh93cT58nQf8iSTDsOmR9PK4PfwxtPgzx1UT9/Dk08T0YQdNdHIWw91WlM3DRbyE6S3l+IDvFYnIAkNrqyY7JZZsD8otqgNM77buC1+vlxhtvZPny5QDodDrefPNNrrnmmm5tx2AwtLvJH3roIdra2ti8eXO7I33Lli2dYkGOHj3K0aNHefbZZwHIzc3t5EhPSOiH97iX+Oc//9kuYN92223k5OT0ynb1ej1ZWVlkZWWdcRmPx3NaYd3v93PzzTezZMmSXq3DIhAIBAKB4PKhR47zk0VzUBwZP/rRjxg/fnyvNk4gEAgEPSPgC6HVK0Kx2dZPw+cBjU5N5phYMsfE4mz0UlXQRGVBM21OPxVHGjFH6tuFc1mWaXMG2PJeIX5PEGu0kQlXnb3I55mIjDMxZWkWRqsOrV7NgbUVNNW40ejVTLwqA4O5B0OyZRkOLCMUlmlImkO0yc6IL7rNz4Q1HkbdCiNugqrdcGylclt/RJl2PA8ZMyBrHlj60IXucyrCF/RvYdCCTxVXauLoi6IYaDgss3y/UgfgmtFJLBmeiO4sIyRO4PIF+eRADf/ZUExChIFB0b1feLPU4eazQ7VsLe6cX75gaDxTs2IwaIV78lIl5HKjMpuQJAlV9lTs334IedfLGK+4G6mvRfPmMtj3ujKKBkBSK+eS6DOLl/2G3gK2VGgpZ2iWBY0UTYWsJxAKo+1BB7Hb7Wbp0qV8/vnngCJ+v/vuuyxatOi8m2o0Gpk7dy5z584FFIH3hJC+evVqtm3b1klIz8/PJz8/n3/961+AEi1ywpE+a9Ys4uPjz7tNfYHH4+Hxxx8HlM70n/3sZ/26f5PJRE5OTq+J9QKBQCAQCAQn6PbVbEREBGVlZeTl5XV6vry8HKvVeoa1BAKBQNBf1BS3sG9lOWMWphE7aODOy1a7gdzJieRMSqCp2kNlQRNJgyPb51cfa2bP5+XIYRlzpJ5J12R0KxP9i0TEGNvvZ42Lo7mujSFTE3tetLRqNzQVo9MbWHjtV5ilsXTfsaZSQ8p4ZXI7jrvQVysZvQWfQMEnqKIyiG01Q2AO9Hbmau1BQIaIZEXI7g8CXtjzspKrPv8hiBvSP/s9D3aWNVHT4sWoU7NwaEKXRHOAG8emUNnUxoHKFp5adZRfXj2UiF4oUCjLMnsrWvj0YA35Ir/8skSWZRqefhrL3LmYJ00EwDh1Hkya2bn4pqtOKUrcWx1Uzhql6GfpJkAGJCVqasSNYInrnX30BrG50FJORrYGu5yK0xuk1OEmO657v3lOp5Mrr7yS9evXA2A2m/nwww+ZPXt2HzRaEXjnzZvHvHnzAEW037RpU7sjfdu2bQSDwfbljxw5wpEjR/jnP/8JwNChQ5kzZ057tEt0dM/rlvQmzzzzTHsW+U033cSQIRf+eV8gEAgEAoGgK3T7X/Ytt9zCvffeyxNPPMHUqVORJIkNGzbwwx/+kNtuu60v2igQCASCLuJs9LLnszJCgTB1pa0DKpyfQJIk7Elm7EmdI1qqjrYgh2WMVh2Trs1Eb+o90dho0THtxuyeD80+7jYHYPBCMNg4b9++OVoRn4bfoMSnHFsJFTuQHIVk1tehfr8AMqYrLvTorN7JI68egJiWiu2KaG6JU6JpLnBkWWb5PsVtvmBoPMZujHhQqSTun5XJwx8epq7Vy99XF/KDhTloehiLJMsy64428PGBGupave37mJAexYKhCWSI/PLLBs+27fhLS2n98ENM48chnchlPlk0b2uGzx8CawJM+zYYujgi5kyEw7DqYaVjDyB1Eoy8WanjcKERmwvHPkdqOEJ23GR2lzVzrM7VLeG8ubmZxYsXs3XrVkAxB3300UdMnTq1r1p9CmazmQULFrBgwQJAyQk/IaSvXr2a7du3EwqF2pc/dOgQhw4d4m9/+xuSJDFy5Ejmzp3LnDlzmDlzJjbbeX4GekBbWxu///3v2x///Oc/7/c2CAQCgUAgEPQV3RbOn3jiCSRJ4q677mp3RGi1Wh588EEee+yxXm+gQCAQCLqGo9LF3lXlhAJh7MkWhky9sIuMjZqfiqMyiqgEM3pj78d5nFeeac1+ZMdR6twy2kEL6VWvtiQpQnbiKPC2ED66irY1L0LQC4WrlClyEGTNhfQZSixBT5BlqNmr3E/sx5iW4rXKbcas/i1G2kP2VbRQ3uhBr1Uxb0j3YxBMOg3fmpfNI8sPc7TWySvbyrhrSnq3t9PSFuC59UXtOetGnZpZObHMGxIv8ssvM2S/n5b33wPAPG1qh2j+RVorwe9SRpZ88v9gxvfBntm9nXlbQGcFlUqZhi6Fyp2KYN7dbfUnMbnKbVMJWYM17HC5OFJYzeLhXfvda2xsZP78+ezevRsAu93Op59+yrhx4/qqxV3CYrGwcOFCFi5cCCiO+I0bN7YL6Tt27CAcDgPHR6bs3cvevXt58sknUalUjBs3rt2RnpWVhd1uJzIysk8LYj733HPU1NQAcP311zNiRM+z5gUCgUAgEAguNLqtVOh0Ov7yl7/wu9/9jsLCQmRZJjs7G5Op93M9BQKBQHBuPK1+jmyupvpYMwBGq46xi9JQ9VEx0N5Cq1OTkNH/7rgucfBt3L4QH3uHs/WTCv50cx9lSRtsyHlXsa9QImVCJqrSdVC2WckX3vkC7PmfUkg0a64SedIdIdpZrThHVRqI7adh855GqDmg3E+f0T/7PA9kWebDfVUAzMmNw6LvWQdOos3I/TMzeWrlUdbm15NqNzEnt+uxFgerWvj3+mJa2wJo1SquG5vMrJxYkV9+meJcu5aQoxF1ZCSW49nYpyV+GCx8FNY9Dq5a+PinoLMooz1OuNBP4GkErQm0x6OrfC6lYHH+CpjwVSWOBSB7Pgxe0HcH11tY4sAYBd5W4rd+gr9YR4EnGlkec85O02AwyC233NIumsfGxvL5558zcmQ/djB2EavVyuLFi1m8eDEAra2trF+/nlWrVrF69Wr27NmDLCu1D8LhMNu3b2f79u384Q9/6LQdm82G3W4nKirqlNu4uDhuueUWkpK6X2/D5/N1cpv/4he/OI+jFQgEAoFAILjw6PIVosfj4Yc//CHvvvsugUCA+fPn89RTTxETE9OX7RMIBALBWfB7g6x7LZ9QIAySxKChdnInJaDrAwf3ZUPtIag7TK0ryE7TDCam2/tewJQkRRhPHgnj7oaS9YrzvLlMuV+yHqyJx13o00Gjh3AQwqHjtydPx587UdQvNrdDLOtritcBshLRYr0wi9idzKHqVorq3WjVKhYOO79iiyNTIrl+bArLdlbwytYykmxGchPOHhsRCsu8u7uSjw5UI8uQHGXkgVlZJEUaz7qe4OIkLMssr2+hwO3l7uQYonWnnqdDLjfOjz4CwHbN1ah05xhtEJkKi34LW/4BlTsUB3qjC/zuzsut/yM4joE+QhGdW6sg4FHmVe7sEM4vglEigNLOOT8DcyyZO/aiObafVmcbta0+EmxnP9/99Kc/bS8EGhsby7p1606p3XShEhERwZVXXsmVV14JKM75devWtQvpBw4cOO16LS0ttLS0UFxcfNr5Dz/8MJ9//jljx47tVnteeOEFKioqALjmmmsYPXp0t9YXCAQCgUAguNDpsrLyq1/9ihdeeIE77rgDg8HAq6++yoMPPsibb77Zl+0TCAQCwVnQGTQk50ThavYxbHpSpwKZgh5yYBm+YIjNjMCttp23oNpt9BbIXQI5i8FRCIUroXSj4iDf8z9l6g4J/eSilOXjwjmQOat/9nmenMg2n5kTi814/hn7S4YnUN7oYVtxI39fc4xfXDWUGMvp0/EdLh/PrCviWJ0LgFm5sdw6YVCXC5NeyoTDYco9zaSaIlGpLo3Xo8LTxI8PbEfWZaBWqVlW28jXUk8dleD85GPCnja0SUmYJk/u2sb1Fpj1Q/B7wF2vTOFQ52X8yucMX6syAdhSYeQtSvHii5HIVAAsOdmkhLdQ0qYmv7KJBNuZ41pee+01nnjiCQA0Gg3Lli27aETz02G321m6dClLly4FoK6ujrVr17Jp0ybq6upobGykqamp/bapqalTZvoJmpqamD9/frfEc7/fz29/+9v2x8JtLhAIBAKB4FKky8L522+/zXPPPcett94KwJ133sm0adMIhUJ9mpsnEAgEgg6aaz0c3lTN8FnJWO2Kq27ojCRUKun8Mr0FCvUFUHuAOleQHaaZjEixDZz7V5IgJluZxt6liOfHVkFj4UnLqEGlVuJY2icVqLTKfYOt/0Rsv0spXKjWKkUFL3CO1jrJr3GiVkksHt47nSOSJHH3tHRqWr2UOTz8ddUxfrIk75QRCztLm3h+YzFt/hAGnZq7p6YzIb1Xk/QvWj6pyucfxUco9Umk6mR+ljeaSTFpA92s82JF5WEePZqPJ6wi2lBJomUQqxoamWIJMCIquX25kNOJa80aAGzXX4/U3U4DnQl0aRB1mtfr6r8oLnRXnRLhpNZAwijlfHGRo7bbydSHKAnK5OeXM2vo6YXzvXv38pWvfKX98Z///GdmzLjwI6W6Q1xcHDfddBM33XTTaefLsozT6WwX0xsbG3nooYfYsGFDt8Xz//73v5SVlQGwZMkSxo+/SDtgBAKBQCAQCM5Cl4Xz8vLyTn8uJ06ciEajoaqqitTU1D5pnEAgEAgUvO4A+VtqqDjSCED+lhrGX5EOgPoCzzK/qDiwjGBYZnN4GE51FIv6221+JrRGJXs4ez4E/SCpFMH8Quos0Vthye+VLGWdeaBbc04+PO42n5Yd06vFN/UaNf83dzC/+eAg5Y0ent9YwgOzMpEkCX8wzBs7yll9pA6AjBgz98/KItZ6elf65cTn1QX8vfgIxV4A5XNd7pd4YN8ebk918vXMIZgvMqNGMBzikYPreK++FVARrQnzRN5gXqysYU1TJb9v8/HfKTe2d3qqrVZiv/Md2nbvxjBsaO83SGcGe4YyXSrsfQ2peh/ZcUmsqoKjZfWnXczhcLB06VLa2toAuOeee/j617/eny29IJAkiYiICCIiIkhLUzpYJk6cyBVXXNEt8TwYDAq3uUAgEAgEgsuCLqstoVAI3RdyFjUaDcFgsNcbJRAIBIIOSvY1sOZ/R9pF8+TcKIbPTD7HWoJu4yiE6j00uPxsMcwg1W4i7xwZ1QOCRqe4RS8k0fxkTBe+c7qkwc2ByhYkSWLJiN7vHLGbdXxzbjZqlcSOkkaW76+mpsXLb1ccbhfNFw1P4CdL8i570fyo28uvj5bww8OKaK5CZrpNz1PDhzPSrEKn1rPXY+RH+RWsb3S2F0K80KnwNHHLluW8V+8EJCZGaFk2ZRGj7cncmZKKJEkc8mrZUNM5k1qflUXkjTeKEURdpaEAGgvJi1GuR6qbPDi9gU6LBINBbr31VkpKSgBFKP773/8uXuPjWK1WVqxYwfTp04GO2JZdu3adcZ1XXnmFoqIiABYsWMCUKVP6pa0CgUAgEAgE/U2XHeeyLHP33Xej13dc4Hm9Xh544AHM5g5n2dtvv927LRQIBILLGEeli4PrKwGwxZsYNj2JqIQL3817UXJA+f1yxE7E643lxmHxQljpKq56xc2qMw10S7rE8v2K23xypp04a98UTs2Os3LH5DRe2lTCO7sqWb6vGn8wjMWg4avTMxmRYuuT/fY37oAPo1rb7Szy7Y4q1rRK7HMqDuAYQxQ5ejffzRlHtjUWgBlxmexsauKNOhdVvgD/Kq/jr0e38J3MwUyJTe/tQ+k1VtcW8/PDe/GEVWgkma+mJnJ/9sT2+eOi4hgdYWV3SytPFe5lStwQJE8bausF2FE3gMiyjCRJFGzZSMOuLTSOHkl82hfc8jG5UHsQe4SbuLCWeo+Ko7UuxqZFtS9ycjHQuLg4li1bhsHQTwWTLxJOiOddcZ6HQiEeeeSR9sfCbS4QCAQCgeBSpsvC+Ze//OVTnrvzzjt7tTECgUAg6ECWZQq21QCQkmdn5NwUIeT2FU0lULkDkBg2/8s8YYxHJyJwus6uF6B6L0x6ANKnD3RrzkpFk4ddpU1IElwx4sxFBHuDWTmxlDd6WH2kDn8wTF6ilftmZBJp6r1omP7GFfTzWXUBmxzV7He6qQ1IqCWIVMvE6NRkmiOZlTiSWJ2GOJ2WSI2MRdNhuthcX8JfCveT75HJjszGpDUwI8rKtbmTiNWf+rqMi4pilC2STxwt/Ku4gHJ3G1/fv5fptnx+MWwqcYYLR2yWZZn36pp5syaIHx3RGj9PDJ/AaPupI4S+kz2Kr+zaRKHfwsp1bzPs7Q1YFy7EdvVVA9DyCw9HRTkH1nzG5OtvoaWuBn9rC46KslOF89gcALRSNXmjJ9LSpqewvkM4f/XVVzsVA33rrbdISUnp12O5WOiqeP76669z9OhRAGbPnn3J5cQLBAKBQCAQnEyXhfPnn3++L9shEAgEgi8QDskYLDrUmjZyJyUI0bwvOe42Z9AksCVzcfimLxC8rVC1B8JBiBw00K05JyuOu83HpkX1S+HXWyekYtarseq1zM2LQ6W6uL7H3lCAPc0NVAUNHHS1cczdxn5H8fHIFKVzKSSDIyjhCMpUB0OUy4729Q86DmKSQtg1EhqVdDzDHEAmU9vK93IGE6/XnrUNGpXElbGRDDVk8ZvD9exxhdjQ4uPaLSv5UlICD2RP7Lbjvbep9bp4ocrJAVcbKknNLam53DcoCZv29J+xkbYoxkXa2dZYx4rdR8jz+5B93tMue7nRVF3JprdeIejzcnj9GuzJitDdWFl+6sIxOYCE5KlnxPhstm9v4FidC4A9e/Zw7733ti/6l7/8RYi85+Bc4nk4HO7kNv/lL385gK0VCAQCgUAg6Hu6LJwLBAKBoH9Ra1SMWTAIX1sQvVGcrvuMlgoo34bHH6Q6bhGZx+MBBF2kdJMimkdlXPDCeW2rl23FSq2Aq0Yk9cs+NWoV1425uByu2x1lrKwtZVdLM8XeMGqVnsFROcfnSqQYLSRpvEy2xzIrNgNvOMgxl4MSdwt+yYxWZ6bOH6CyrQ1ZlnHLKtz+E1uXGWvV8O3sMYyM6t57kGGN5vmJV7Ki8jB/KszHEVTxbEUdH9S+zzh7Mum2dACC4SB76vaccTtRBjtZkZkAhGWZXbU7z7isTW9jcNTg9sc7anYCp+asH3D5iDSlEm+yc3dSDDPs53bCfzN7OF9fu5KCyDSO6UqZvXjJOde51Gmpq2XTm4poHpOaxvC5C2mqqQKgqaqyPb6lHZ0ZIlOhuYzBqhpAQ2G9i02Hyrjjuuvai4F+5Stf4cEHHxyAI7r4sFqtfPTRRyxZsuQU8bywsJDDhw8DMH36dGbPnj2wjRUIBAKBQCDoY4QSIxAIBBc4QjTvYw68DcjsCufw/CY3N/hq+jzC45KieJ1ymzFzYNvRBVbsr0aWYWRKJIOixbiCk/GHgrxeto/XK8up9J/ccSShlb2MteoYHRHBMIuROH3mKesPscWf8lw4HKbel0CBs54iVxMN/jbmx2cwKur8ihtfkTyEuQnZ/KVgM8tqGqkJqNjU4qU05AQgFA5xuDVwxvVtPi8VYWVZWZY5eJZlrTofVbKz/fHBVv9pZHMAFWZfFQ+NGE6qsWsFX4fr9YyrdHBYp2fNvAXMs1ze9SucjQ1sfP2/+Ns8RCWlMPnG29BotUTExiOp1fi9bTgdDUTExHZeMTYXmsuIaT7IBFUGG8ud3L9hHWV1TYBSDPRvf/ub6BDtBhaL5RTxfNFt92MZOR9dYg7+6gJ++ctfitdUIBAIBALBJY9QYwQCgeACQ5Zl8rfUkDrUjtnWNQFG0ENaq6B0E22BEB+GJiOpYfxJReUuKwJtsOUfMPp2sCZ0bZ2WCmgsBEkN6dP6tn3nicPlY1OhEiFy5UjRMXICdyjE2kYn/yjcTYXHCUiokBlsUjHWFsmcuDTGRaX0KApFpVIRb7QSb7QyI653221Qa/nxkJncmtrIG+WHMOtspFrtAITCQQ5oz1x8NVIfSVqEsiyyzF7NmZe16Wyk2+ztj/epbacVzm1aHXdljMam7fo527lqNVcXFVExdirlGXk4/EGidZfnX3N3cxMbX/svPo8bW3wCU2+6Ha1OeS1VajW6COU9clSUnSqcx+RCxU7QGFi8ZwXbG6yoA2psU25Bd2g5b7/9tigG2gNOFs+3FzcgDVsEkoaI0YvJTLMzf/78gW6iQCAQCAQCQZ9zef47FwgEgguY6mPNFO6qo+xQI/O+PAS1RhSpBCAUAPXZs5C7hd8DG/4MyByWsqnXJjF2UBRxEZepwLLzRSjfCvX5MPdnXYtdOeE2TxoNhjOLjxcCHx2oIRyWGZIYQXacZaCbM+Acaq5hQ0uAbc4gvrCMVhuDUdXCXLuVr2WNYZDZfu6NXACkWez8cMipBWmXJszq8jauju/GsnFdX/ZshFwuWj/5mMFtXoYlxVMkSaxoaOZLSTG9sv2LjV0r3qPN2Yo1OpZpN9+JztA5G14XEQVeJ46KMjJGj+u8cvp0SJ+OSpLYs6aGvPeepXr8LXhjkrnudy+SlNQ/sUyXIhaLhd8+8xpf+9MbWFxuItQqGm0x3HzHz4XbXCAQCAQCwWWBUGMEAoHgAiIclinYVgtAxsgYIZqfoGgNvHUPbHwKQsHz314oCOv/CM2l+DVW/hecC8CiYafGTVzyNByFyp0wbKkilnub4fNfQ8Oxs68ny1CyQbl/gce0NHv8rD9aD1zebvOwLPNZTQF3bPmAO3Zt5Y2qMnxhmWSDjm+lp/HZtCU8MnLeRSOaX8z4i4shGEKXksKNo4YC8H5NFa8dXT7ALRsYxl25lPjMbKbd+iX0plMja7S2SAxmCzrjaYqtShJIEgUFBdz+q1+yur6aSQc+YWhUFDVuDZ8equ2HI7g02V7SyItbKxmblMRETxXDC7cQbzRRSyyh8OlDiwQCgUAgEAguJYTjXCAQCC4gKguacDf70Bo0pI+6PJ2Hp1CyEbb8E5ChdCOEAzD126Du4U+YLMPWf0DtAdDoWZv4FZoLtWTGmsmOO3dBv0uOI8uhbDMMvwHm/QrW/A4cx2DVb2DWjyF+2OnXkyRY8LDyniSPO/0yFwifHqwlGJLJjrOQl3D5vcfeUIAXi3fyoqYFT8Gx405RiThVKz/KGMNwi1G4R/sJz65d+I4ew3bVlST85teE3W7irUZSdGE+bijhv02lTIkvJS0ibaCb2uecXOjTZItk6k13nHFZfVQ0C+74Ejqd7ozLvPDC80jBNj5qg+tSIslVN/BBWRlvbNaRGmViaFJErx/Dpcz2kkb+teYY3pJSRjeXcvMgNfWp6TwTnUS9y8/WIgdTs8X/FIFAIBAIBJc2wsooEAgEFwjhUJijx93m2WPj0OrUfb/TmgNQsx/C4b7fV0+o2AGb/6qI5WodBL1Qvg02Ptlz5/neVxWntKTGP+XbfFChOBgXDutirvelhCxD7UHlfsII0Ftg7s8VsTzoU0T0yp1nXt8cDUOv6d0InV7G6Q2wpqAOgKtGJl1WArErGOKxwxuZv345/yivpUltRCPJTLXpeH70OF6ZtIQRVlOn1yRQW4tz5Uq8hw8jywPnKB3IffcFst9P439fxvHMs7hWr6bl/ffRREWhS0lBkiRuT04iUh9JjZTCqwUfXHLH/0UCXi/r/vc8VQWHu7S8JEln/+5W7mR24yv8cKqSi37Vv/7FzGiJsW3V+EpL+eeaozS4fL3R9MuCbcWN/GttId6SUsY0FXNLuIL4r3+dcX96gqU3zkal1/Ph/mrCwnUuEAgEAoHgEkcI5wKBQHCBUH64iTanH71JS9rw6P7Z6b7XYdUj8N7XlYxrR6Eipl4IVO2BDU9CyK8Urgz6IBwEv1sR1Df0QDzP/xgOvafcn/Q16q1DMevVRFt0jB10GRYFbS4DX6vSKRE9WHlOa4RZP1Fc5KEAbH8Ogv6Bbed58PnhWnyBMIOiTQxPvjwcp9U+P89XNvDtI2V83gzusAqzKsxEv5N3xs/gb+MWM9qefMp6YY+HmocfpvnNt6j/y1PUPvIo7s2bkYO9EI/URQJ1ddT+/g8UL72Omt/9jlBLS7/tu68I1NRQ+/s/4N64kbDXS9jjxrV+A4HajgiR4RYjk6KTkSUN61vCHHQcHMAW9y31pcWsf+1FGivL2fvZRwT9XT+/yLKMv81zyvM+lZmA00FejIrs7CwS09OJ/fqDXK+qIS1Sj8sX4q+rjuELhnrzUC5JthY5eGZdEbIMUwdFcFO4grhvfB3DkCFIOh1z8+Iw6zXUtnjZXtI40M0VCAQCgUAg6FNEVItAIBBcAISCYY7uOO42HxeHWtuH/ZpBP2h0iss8chC0VkJbE+SvUCZrolJsLW0aRAxQHnTtIVj/hCKW+z2gt4JKA9HZMOpW2PRXqNwBG/4E07/bNcdz2VbY+YJyf+QtkDmbZODRpSNwuP2oVZePE7md2gPKbdzQztE3Gh1M/x7sfB4GL1Qen0zxOsW1n7MYksf2X3u7gSzLrC2o55MDyvfqqpGJl7TbPBwOs6L6CC+XFeLXpWPWKjnRoyPjyY3VcnPyMNZ8+jkJRqXzQJZlfAUF+I4dw3bllQCoTCZMo0cTamnFX1ZGoLKSxhdfouXd97DMmY15+gzUllPzp3uDQEMDjc8/j/Ozzwm73QC0frgc98ZN2O+5h8hrr0HSXHx/W91bt9H0yiuEvW2E3R4knY5gTQ2qtjZa3nmXmAfuBxRH9W3JiexsqqWmLcjrRz9kaPRQVNL5/Rb4w2EcgSD1fmVq8Aep9wcwqFVcGxdJrK7/Ros4Kss5vH419aXFAGgNRqbceDuas8SvfHH9XR+8jTHCxpwv39dp3vZjtXj8YcxaWDpbiY7SxseT+stf8H2dmd98cJDyRg8vbirhvhmZl/S54HzYWuTg2fWKaD4tO4Z7po0ndN1ENFEdHcsGrZp56RaWrTnM+9tVTMywi9dTIBAIBALBJcvFdwUiEAgElyIypA2LprqwhdRhfViYr3QT7H0NZv5AEc0n3gfj7oHqPYoQWrkDnNWw/03FfT77x33XljPRcBTWPqaI5gGPEh+iM8O8X4ApGgw20Jph3R+gfCus/xPM+N7ZxfO6I7DpKUCG7Pm0ZFyF7fgslUoi1qrvjyO78KjZr9wmDD91nlqjfD5OxlUPllgoXA11hyA274IUzj3+IC9uKmXHcTfk6NTIXh9REA6HUakGfuCeJ+jnpeLdvF1TTX1AaU9EqIEZ0bEsjrGRZzYgSdkEAgEAgg4Hnh07cW/eRMihvD6m8ePRxiuFce333oskSYTdiivatXo1oZYWWt59D5CIWLyoV9sfbGyk9aOPcK1eg/ewEtuhttkwjh2LZ+tWQk1N1D/5JJ6tW4i+96sYcnN6df99Rdjvp/m113Ft2kS4pYVwWxua6GgkrRbj2LEE6+po27MHX2Eh+qwsAEZajEywJ/FpVRO7PUa2VG9hatLULu2vxhfgkKuNhnaRPECDP0jLWRzWW5pd3JxgZ350BKo+FD5djQ72rfyE2qKjAKjUatJHjSNnynSMlq7XHDBF2PB53PjaPAT8PrS6jvP2+g2bCDnCjIpXsWD0oPbnNVFR2IEHZ2fzxCdH2HykhrRoM4sux2iuc7ClyMGza48RqKll9uQ87pmWjiRJnUTzE4zevIL36o2UAztKM5iQLgoKCwQCgUAguDQRwrlAIBBcAKi1KgZPiCd7fFzfObcai2HL35X4jdJNinAOikCaMl6ZAm1QsV0R0TNmdKzrqodtz0D6NEiZCDpT37Vx9W8h4AWfSxHNtUaY/VOwZ3YslzgS0qbD5qcVgR1ZcUh/0RkN0FKhiOzhICSPpzTjFn7/7n6WDE+85F3IZyUUVMRvgPjTCOdfpPYQrPktZM7uWC9jZp81r6ccq3PxzLpCHC4/KpXEDWNTWDQsvkvvszvgI7+1nlqfiwafB4ffSwg90ZY0nMEQzmCItdU7cAbDtIUlUnTw8qSF2LTGfjiyzlR5mnmmcDefOlpoC6sAFRpJZorNwNcy8xge2SEMhv1+PNu3E/3xJ9StWNHuYpaMBkzjxyOpO+opnHidVGYzEYsXYZ0/D8+OnbjWr8M8fXr7cr7CQgB0mT1z7wabmmh+403a9u+DYAhJo8GQl4tx3Djsd9yB2mbDX11N3R//iO/gIUKtTuqffBLThPHYrr/+tGLehUKgpgbHM8/iKzxGoLoGldmMJj4eTaSNiGuuwTxlCk2vvIp7wwaal71N3A9/0J7hfVNiHLub66h2+3mn8BMmxE9Ae4ZOQVmW2ets4zNHC/ucbWdsj14lEavTEqPVEKfXEK3VsLPVQ4Hby3+rHGxqdvHVlFhSDF1zfncbSaKupBBJpWLQ8NHkTZ2ByRbZ7c0YrRGYbJF4WpppqqokLr3jN2HDhg1ENoQYFa9iTOqpv0/ZFhWLq/fwdrOBN1QqBtlNDEm8PKKbusKmwgaeW1eIr6SUcU3FXLmrEGZ/54zLxy2cz/R/vc3nDi3v7yhlfFrU5ftbKhAIBAKB4JJGCOcCgUBwAdFnF57eFlj3hCKaJ46GETeffjmtURFDvyiIlm6Amn3KpP63kn+dPgMSR/VeYciWClj9qOIyt2cobXXXwawfQ+wXXKahIDQWQWSa4lAPtCnZ7DO+31k89zTC6t8puejR2dSPvJ8/f1qELxAmv8bJkuEJaNSX6cV+U7HS6aCzQFT6uZdvLFTek6OfKY/jh4E5pk+b2B3CYZkVB6p5d3cVsiwTa9Vz/6wsMmK6Fi3S7PNwzZZPcYY6u8iNGiNZvg6RtimoJRBW3NsVfvjmrpW8OOGKfnOfl3v9fNLQwvNFO/EE/YAKiyrMktgo7ssaR6zBcso63n37aH7pJfR1dRAXhz4vF/OUqRjHjEZ1jpgMSaPBPHkS5smTAEWsDdbUUPfkn/EfOwZqNZrYGDTx8WiTktClpaFLS0cTG4smNgaVvvNojlBzM46XXqJ1xUeEnU70uTkYR4zEdvVV6LKyOp0DdYmJJD/+OKH6epwrV+Jatx731m20vP8BUXfcTuT1119w8S3uLVtpevVVwl4vgepqNHHxaKKisC5cgHXhwvbXw3bVlXi2bcNfVETb7j2Yxo4BYLTVyNioBNb6ncTGXYtGderxuUMh1je6+NzRSq1f+SxKwBCLkSS9llidhhithlidct+sVp3y27IkxsbKRievVzdS6PHxi6OVXB0XydWxkWjPM7bK1dRIXUkhmWMmAGCJsjN60VXEpAzCYj+/+h3Ryal4WppxVJa3C+ehUIiNGzeSbgijH6snJlTbEUl2HEmtYkpbJcVtJvaUavnHah2/vGY4MZbLdLTRSZwsmo9vKuYGqrBd/Y2z/h8xDBvKnMTlrG8IUnqsgt3l6ZdnnRCBQCAQCASXPBfW1YZAIBBcZgR8IXZ9Ukrm6FhiUi19I5yHgkqciacBrAkw7VvQXZEvbRqEQ4oT3VkNZVuUSWeG1Mkw8iYwnsdFs7NGKVLqc4I9C+b+TBHCm8sgfuipy6s1sODXsPmvymPHMTj8vuIqn/VjRTDxe2DNY+3H7Zz8fZ5cVUprW4BUu4lvzMlGox74qI0BIzobrnoSXLXQlc/dkKtBpVVyz+GCcps3e/w8u76II9VOACZl2vnS5HSMOvU51uzgH4U7cIZUqCWZCLWMRS0RoVYTZ9AwNS6SCI0aq0ZNa2yYSK2WqjYnjxwt5IA7zEMH1/KbEXP66vAIh8O8V3mIw/5IDruVQoo2QzQ2fyU3J6Vwa9ooDMc7sELNzbi3bkNlMWOZNg0A48iRaFNTcSYlMfz++zEmdC+mIuhw4D1yBF9+Ab78fILNTQQqqwi5XCDLhJqb8R091r68ymhEn52t3Ldakf1+NLExoNHiXruGUKvz+DwLEVdcSdSNN5xx35IkoYmLI+q22zBPn07Nw48QbGig/qmnaf1wObHf/x6mUaO6dTx9Qdjvp+nll3Fv24aEhCEvj6jbbsVfUoLt2mtPccirIyOxLphP6/IVtLz7LsaRI5A0GiRJ4ob4aMq8QQoCEu5QGItG+RxXev186mhlY5MTX1gp4mxSq5gZZWV+dATx+q53YkqSxPzoCMZYTbxY1cDuVg/v1Dax9bj7fLDZ0O3XwNPawpGN6yg7sAdZlolOHoQtTokASh85ptvbOx32lFTKD+2nsaKs/bmDBw/S0tLCERcYI+OQ2prgyIcw/Pr2ZVRGI7Fff5AbHvs9tU4jtSVG/rrKwE+vyEOv6fp54mLAGwhR6vDQ6g3g9gVx+0K4/UHcviAefwiP//hzviBufxCvL4i/tJQJTUVcL9UQ+41vYMg5eySSJEkkXL2Eqc+8x2qHlve2lzImNVK4zgUCgUAgEFxyCOFcIBAIBpCiPfU0lDvxugLMvDVHsQ32Njufh/ojipt85o8Usbu7WOJgxI0w/AbFqVyyEUo3KkVFS9bD2Ls6lvU5FRdzVy+g3Q2w6mFoawSdFeb8tKONpxPNT6A1wowfQFQG7PiPIp4fWAYhP8z+CWx4EppLQR+Bb8aPeWpDDbUtXuxmHd+ZP7hbouoliSRBRJIydZXcxYrLvKFAicq5ANhX0cxzG4pxeYPoNCrunJzG1Kzobgk4zoCXD+oaARX3pSZyf/bEMy4rR2TiPXiQ4S4XtSYD//L4+KDeSV7JHm5PH33+B3QS7oCP/xTv4r2aWhxBFcmWZOyGKMbbzCzKTGCwaRwqlQo5GMSzazfuzZvwHjwE4TCa2FjMU6cqESA6HbE//jHbV6xAE31ux2/I5cKXn6+I5UfyCdbXd5qv0umxLpiPbtAgwh4PgdpaAlVVhGrrCDY2orbZUJnNhN1uQk4n3gMHlI6wE+tbLFgXLyL67ru7FbmiS00l4Te/UTLPt2zBV1hI5be/g2X6dPR5uUhaLSpzh+M+1Nx0xm1JGi0qy0nLtrSAHD7DshpUJ2Vxn25Z98ZNeA8dQpOQgP1LXyLiiiVI5+igtC5YgGvdeoJ1dbg3bsQyaxYAYyJMDDLoKPP6WV7fgsddwJqmNoK6FFSSct5KNuhYGB3B1EgLhi50AIbCIZp9zTR6G3F4HejVekbGjCRap+G7afFsbXHz3yoHVb4ADxdWsSAmgpvi7V3adpvLScHmDZTs3Uk4pGSqx2cORqXu/XNsdLISMdZYWUE4HEKlUrN+/XoAfCEoS1kK2dlKR98X0MbHk3DP3dz1j3/zVIOO4kIj/9lg4GszMy/q4tD+YJjCehdHalo5Uu2kuMFNKCyfspwshyEUArWm/fwY9ngIVlUxpbWEa6Ra4r75DfSDB3dpv4bhw5kbv5yNjQGKj5WzryKdUamRvXloAoFAIBAIBAOOEM4FAoFggPC3BSne2wBAzqQEpL64cC9aA8c+BySY+i2wJZ/f9iRJyRq3Z8LoO5Ssa1cNaE9yJ678jeJOT5+uTJa4M2/P06gs765XMs1DQcXVnruk6+0ZcaMSNbLqUag7qIjnLRVK5ItGT2jWT3hmp5uiejcmvYbvLsgh0tRHWb6XAyfy8AeYQCjMsp0VfHaoFoBUu4kHZmWRYOu+U3Z9k4dIUzL2hnKW7irGsf4QYaeTkMtJ2OlCP3gw0V+5R1lYkmj4xz8hHOYqrZbC62ezUtbwZkOIafE+0oznH/1Q6mrkX0W7WdXoxHc8v1wrhRlj8vKtnFRidYqz2F9RgXvjJjzbthF2u9vX12VlYp4yFcJh6IZ46SsqwrlyJW279yjrnkClQpeejiEvF31uHvqMdKQzRLzIoRCyz4fKZCLs8eCvrqbpvy8TrKkh1NKiZJjf/WW0MT2L+dElxJP02O/wbNtG/V/+gr+0DOfq1ThXr0YdEYEuLa192bb9+8+4HZXFgj4jo2PZgwc7H/PJy5pM7QU8AbyHDyMHg6csJ2k06LMyibjyii513KgMBiKuvILm116n5cPlmCZNQmUwIEkSS+OjeKq0lvfqHBQ0OgjJIbSqo8yJSeCutDyGWoyd9iHLMk2+JhxtDhq9jYpA3ubA4XXgaHPQ5GtCljuLqYnmRG7MuZFcey6TIy0Mtxh5pdrB+iYXnza0srPFwz0pMYyynr6mRcDnJX/Teop2bScUVCJjYtMyGDJ9DtEpqec8/p4QERuHVm8g4PPSWldHZEIiGzZsaJ+fN+dmGDfujOsbR45k0FWLuHP5Wp6t1LLteHTOfTMyLpoRSIFQmKJ6tyKU1zgpqncRDHW8t6GWFqyuJuzBNgxBH4aAF6PPgzHowyiHSL77DmyJ8Zh0asLrVhPetxGTXkPs/32zfaRIV5AkicQrFzHl+RWsbdDy/s5SRqbYhOtcIBAIBALBJYUQzgUCgWCAKNxdTygQIiLGSEJmHxUpSx6nZFEnjITksb27bZUKEoYDJxWWdDugtUqJTNn3ujLF5CgC+qDJYLApy3lboHovHHxXiQrxuUCjV/LSNT0QHlPGw7V/hU9+Bq2VimguqWD6dznQFs2e8qNo1Sq+PS+bpMj+L+R4wVF7CAo+Vt6TtKkD3ZpuU9vq5Z9rCylzeACYNySem8anoO2B8BUIy3zscGLHzNXbj+CvLsf/hWVCTR3OZUmSMOTlEnQ0Eqyt5f8+3ob29tsplyz8ubSWX2cnE9HD6AeHz8PP9q9le6ufMBKgIkId5sq4aL6aORa7vvNoEednn+PZuhUAtc2GafJkzFOnoI2P7/I+5VCItt27ca5chb+4uP15bXIy+rxcDHl56LOzURm79r2R1GokkyK0qkwmDFlZJD70qy63p0v7kCTMkyZheuEFGl99DefHHyMHAmiSkrBMmdy+XNDh6OR0PxlNfDyW6dM6lm1uhtOI4QDq6GgsszqiiUIuJ7LX17lNRgNRt9xCxOLF3RIOLdOn41q1mmBdHc7PPsN2teKUHh9hIs2oo7TNT05kCrJrB2b/Ydy1XtYEhxCXczOxplg8AQ9bq7eyrnId9Z76s+5LLamxG+3YDXbKneVUu6t5evfTjIodxXWDryPGGMPXUuOYEmnhP5UNNPiDPFFcw9RIC3cmRWP9wudaklSU7t9DKBjAnpzK0BlziE3LOMPeewdJkkgfNRYkCY1ejyzL7Y5zi8XCqJNje8JhOPg2ZM4Bc8dIi4grrmBYWRl3HDjKa5V6tptM+INhHpydhU5z4YnnwVCYEoebIzVOjlQ7OVbnIhAKIwOy10vY5SI6KZYhKXbyEq0k7duC9rNVpx/AJknE2bXok5Xf4rasVDyt47HOndOp06mrGEePZn7i52yVoih2tHGwqpXhx7ctEAgEAoFAcCkghHOBQCAYALzuACX7Fbd57uSEvnNo6a0w52eKiNwfmKPh+megfJviHK89qMR61OfD5r8pIr6kAkchcFzQ8rlArVOmcfdA1tye7duWDNf9U4ln2f8mZM+HpDGMAu6elo5JpyY7znrOzVwWVO2C8q2gNV10wvmmwgZe3lKKLxDGrNfwlekZjD6PeID1TU6aAiEsdbVMrKlEl5GBccxo1FYrKosFlcWCOrLz9mO/9S3CbW3U/uEPBKtruO+TT/nrVTdS5w/yp+IKfpKZ1J453lXy3V7+VlbP/jYtYQKk6mRuTUnjxtThaJHwHjpMw+bNRFyxBF1KCgDmaVORgwHMU6ZiGJKH1A13edjtxrVhI641a9o7BiStBtOEiVjmzkWXcp6jU/oBSacj+st3Ef3lu047P+q227q8rb5a9lxIGg22pUtxPPMMzs8+xzJjBupIJSv6h+kJFLX5GWZJR5KH8knpJ3xW8hmHHYf5xcZfEGeKw+l3therVUkq7AZFGI82RiuTQZnsBjs2fYcb2B1ws6J4Besq1rG3fi8HHQeZN2geC9MXMsJq4neDU1hW28QnDS1sanax39XGbbERJFcUkTF6rJI9r9Mxav4SNHo98ZnZ/eY0Hj5nQfv9kpISKisrAZgyZQqak4vF7n4J8j+Cyl0w/6H2YqGSJGG/+27GPfMMCTMn8q8DreyraObpVUf5xpxsDNqBjfEKhMKUNChCeUGtk6O1JwnlPh9hlwtTm5OM1hoyPfVkhpzkzbsL0xilWKrfMBqvWacU5zWZOiazGcnYeaSCcfRojKNH97itkiSR+ZMfsHB7OZ8dquWDvVUMS4oQrvOLEVmGusPKf7S4vIFujUAgEAgEFwxCOBcIBIIBoHBXHeFgmMgEM7GDelnMDXgVYfSEIKrqZxFAZ4asOZA6URHPD3+giLRtTRDygSFSWc4Sp2jnIb8imo++XcnQPh+0BojNhbk/R/a2Iq3+HYy5gxmDB53vUV1a1BxQbhOGn325CwhvIMTLW0rZXOgAICfBytdmZBJl7nnsjjcU4A9HNqPVxnLb6CFEq6/CMGJEuzB9NlRGIzEPPkjdY79HU1TIPTs28tjIMayoLKW+9TBPj1mIqgtFeIPhEO/VNfFeXSsyMM6eztJombkJgwnU1uJ+/0M8W7YoudqAOtKG7uabATDk5JyziN8p+6urw7V+Pe7NW5B9imtaZbVimTULy6yZqK2ic6m/MY4ZjS4jA39xMS0fLsd+5x0A2LQaxmhP/FVXcUXGFZg1Zp4/+DylraUcaTxCVmQWGbYMZqXMYmLiRPTqc4/YqXRV0uxr5sbBNzItaRpvFbxFQVMBn5R8wpbqLSzNXsr4+PHckRTN5Egzz5bVUVBXzyNHC0hpdfBdvZHhQ5X6EylDB/YccsJtDjB9+hfqLuReAcXrobEQtv8bJj/YXntDZTQS9+1vEwd8J7GVp1Ye5WBFM09+XsB35uX0aw2MQChM8XGhPL+mlcI6N4FQ59ggo99DSuF+Mtz1ZIVaiZO9SICk1aLLy0Jl6Iio0qWmokvtm6ic0yFJEouHJ7A6v45jdS6O1DgZkthHo+gEvU84rPxHO/SeUsMGlHo2I27qeq0agUAgEAguYYRwLhAIBP1Mm8tP6QFF/Mud1Mtuc1mGrf+Asi3QXA6jbum9bXdl3y3lULVbmerzOwro2TOV+SfysRNHQ8EncOhdRTQffgMMvbbXmrK/ooXaT//ELO0htM1lsPCRTkP1L2t8TmgqUe7HDxvQpnSVkgY3/1pXSF2rD0mCa0Ync9WIRFTnWRfgxeLd1Pp8GANlzIofhjmli9n6x9HGxRH9tfuof+ppbFs3c93wFB4J+9nUIvHU0S18J/fsbv5ip4Pv7d9IK3aSLElMi7RwV5yN8I4d1L70Lv6iovZlVWYzpkkTMU+ddpYtnh5ZlvHlF2D/fCV1H32E6niIgzY5Geu8uZjGjz9jZrmg75EkicgbrqfuiT/i3rgR67y5aBMT2+e7A242VW1ifcV6Gr2NWHVWBlkHEW+K576R95ETlYMkSTR7m6l113bONz+ecX7HkDvIsCkxKoXNhbyR/waDIgZxU85N/N+Y/2Nfwz7ePvo2jjYHLx58kXUV67g+6zpUJY3M2LwBtdbMDnsKlZGxPNYa4q6GFhZED5yzOODz0lhZwab169qfmzFjRueFLHEw7duw+rdQvBbsGaetn5GXEME3s/X8cdk+8tvSeSIk890FOVj0fXOZ5A8qQnl+bWehXHGUewm73JjaXOQNsjNy4nByE6zE+Fqp/dVrSFoNupws9DmDMeTmoktPR9L08+Vc0N/u3j9BpEnH1CgVn+8q5r2dRoZcdfF0yl7WVOyA3S+Ds1p5rNIoUXsHloGrDqZ+c2DbJxAIBALBBYAQzgUCgaCfMZi1jFmYRl1pKzEplt7d+IFlimiu0kDS6N7d9ukIeJU4lqpdiljucXSeH5EMSWOUKTYP1Cf97GiNYE2AtOlKgc9e4nB1K/9YewyJuWQEq8lqa4Q1v4MFv1bc8Jc7tQcBGWwpYIwa6NacFVmW+exQLW/trCAUloky67h/ZiaD48/fFR0Mh3i9qgpkiatjozBre1bU05CXR9Qdt6OJjeWGnBzyD67lzdoWXqqsJ8dymCuSh5x2vbfK9vNEUSG+sAqN1MAPs7JYHB+HHAxS9d57hJ1OUKkwDBuKecpUjCNHdFsgkwMBPDt24Fy5Cl95GYa6OoiLwzByBNb589Hn5IhIhQsEfXY2xtGjaNuzl5Z33yXmwQdp9DbyUfFH7KjZ0R7HYtKamJY0jenJ04k2dnQG1rhreGTLI2fcfkNbQ7twnmhORKvSUtZaxh93/JEJCRO4Nutafj7p56wqX8UnJZ9wsHwP2/atIs0dxei2NGZY4JqMeD62xnGszc9/qxxsaXZzb0oMyYb+73TZ9OYrNFaWU7B7JwAajYZJkyadumDiSBhzJ+z+L+x6CSLTIH7oKYvF7dnEfa58/l0cojAs88QnMt9bmEOEoXuRS6fDHwxT1OAiv8ZJfo2TwuPFPDsL5U7SW2vI8DSQedxRboodQ0yeEl0mywbivv89dGlpA9fJ5WmEXS8qdUyW/KGTG1mWZSZuW8HnrkQOHS4jf3wauQkDM3pFlmXc/hBNbj+BUJiMGLM4z52JoE8RzXVmyFmsTFW7YNuzSo0cgUAgEAgEQjgXCASC/kaSJBKzbCRm9WIBrZZKRRSo3qM8Hn+PElnSF7RWd7jK6w4p7qQTqLUQP7xDLLfEnXk7w5YqUy8hyzKfH67j9e3lyLLM0JQE0iY/DCt/pTjh1z2u5L13M3v6kqNmv3KbMGJg23EOWr0B/rOhmP0VSkTJ2LQovjw1vddcoC+X7KYpqCK2oZVbtx3CZ0hBn5XVo21ZpnW4wH8yZAaFno/Z5Qzym6P5ZFrs5Nk6inV6gn5+tn8Na5q8gIp4bZjf541lVKziMJY0GiIWL0IOhTBPnHhKvnpXCDmduNatw7V2LeFWp7JdnQ53bh5x3/wGxi5E0Qj6H9vSpbTt20/b3n248w/z96Zl1LhrAEixpjArZRbj48ejPc05bGPlRmUbelunnHO7wU60IZoUa8d7PjhqMA9NfYgPiz5kS9UWttdsZ2/9XhamL2Re6jwmxk/kT6//lEOBWkpMDlpStNw4ehbj0kYyUVKzstHJa9UOjnq8/PxoJdfGRXJVbCSa8xwB0h3sySnUFBfibW4EYNy4cZiOF6U9hbwrlQiKkg2w4UlY/Dswx3RaJPqeu5H//W/u33+IZ0vClITD/P6jMD9clEukqXtCtT8YprD+uFBe66ToJKGcYFDJtTdqGRxjJOa95WQGWog9OXolJ1NxlA/pEPglSUI/eHC32tFrnHCYa/RKUe9Am1LHZFBHR4UkSQy6YgETXlnN1gYt7+8q44dX9P6IppNF8Ua3n0aPv/1+k8dPozvQLpifIDHSwBXDE5mYYUfTgwLSlww+J+R/rHSYD56vPDdoCvjdkDFDMTMAZM5WCsqb7B3rnmaUgUAgEAgElwtCOBcIBIJ+IhQKEwqE0Rl68dTrd8O+N+DoZyCHFKf58OuVwpi9RdAP9YcVobxyF7hqO883x0LyWEUojxs2IBdX/mCYlzaXtOdfT8mK5q4p6Wg0Kpj1Y/j8IaXo1ea/KUP3L2f3We3xfPP4C1c4P1zdyrPri2jxBNCoJW6dMIjZubG95hoMh8P8r6ICVRCWHDmKprpOyXntBUK1tTy05RgPjEihKqzh//Zu5s1JC4nUm9jXVMWPDmyjNqCIN3OiDPwqejCevz9P27XXthfps86b16N9+ysqca1ahWf7NuSA0qGljorCMns2+kkT2b12LZr4+HNsRTBQaBMSME+fhnvdeo789+/UzA5j1lm4f+T9ZNgyzvr5vyHnBpZmL0XdxZoWNr2NO4bcwfTk6byV/xZVx46w3Pc+Va4qvjL8K/zf7B9xsGwPW3UFVLgreK/ofTbVbOaGwTcwP2Y4Y6wmXqhsYI/Tw7LaJra2KO7zbJPh3DvvBqFwiOXFyznsP8wVXNH+fExKGo2NTcRHKKO2Tsk3PxlJgolfg5YKaK1UOlK/IJxLOh0xX/sa0vPP88CuQzxbKlMhy/z+Y5nvL8wlxnLmESm+YIiiejf5NU6O1ChCeSh83FHu9RJ2u7C0uUhrqSbHKjH52/cRH6FHkiTq9kcCkRhyctAPHqxEr2gvoM7d2kOw6SmY8X2IGazkxh9YBgfeUuqYnPSZNE0Yz7wPP2FbW4ADh8o4Nn5Qtwpyy7KMxx86SQTvEMMb3T6aPIoo7g+eeq6Ww2HkgB85EFAmfxCrFohPpLrZy3Mbinl3TyVLhicyLTsGneYyEtDdDjjyIRz7XKkpY4yCzFmKiUClgpyFp65zsmjuaYRPf3H8v2XPfpsEAoFAILiYEcK5QCAQ9BPFexoo2lPP8JlJJA3upYiMUACK1iiiefI4GPMliEg852rnxN3Q4Sqv2a9cbJ1ApVFiV064yiOSBlSIbnT7eWrlUcobPUiSxM3jU1gwNL5DZLJnKBf9ax6Dss1Ke0fePGDtHVACXtBZlPcw7vQRIgNJMBTm/b1VrNhfjSwrTsH7Z2aRaj+Dk7SHvFa2j4agiuhmF4tr69EPHtwrbk5ZlnH8+zmorOSxUJAHR2XQEFTzvX0b+XLmBH6yfxfesAq9KswPMjJZVNxIy3/+hBwI0vL+BxhGjep254Asy3gPHMS5aiW+w0fan9elp2OdNxfjmDFIGg2BQOC8j0/Q99iuvBLnls04CvaTmJrC9CtuJjMy85zr+TxuWupqMVismGyRaLogvsqyjKEhwNhDEdhLrZSl+pg3RRHG4tIziU3LYDawtWYr7x17j3pPPf/c+0+GRg/l+sHX8730eLa0uHm5ykGF189vjlWxMMbGjfFRGHrB2RuWw7x8+GW2Vm2lzl9Hq7+VaK0ST2NPTqGxsZEokxG9Rn1qvvkX0ehhxg/A71J+E06DpNEQfe+9qHT/5cEtu/hXWZiqcJjHPpL54aJc4iOUTgFfMMSxOhcFtYpQXlzvJhSW27cTbGrC4m4hvaWKjDYHmSEnMScc5QED8UZV+/c89tvfvnBjRGoPKr+bIb8ivE7/rpIRf2Q5NJdB5U6lZslxJLWatCXzGPf6BnbUa/lgVwXfXaz8zsiyTFsghMPlp9kTwOH2dXKIn3COn0sUJyyjjojAatAQadJhOLAbS2MtET43kbIfW9iPTfYTIfsx2CKI+uajrM6v49ODNdQ3tPLfLX7e31vFomHxzM6Nw6Dt5+Lp/UlrFRx6H0rWd4wMjEqHoUtB6sZxH/scPA2w7RnlfR97V/8XnRcIBAKBYAARwrlAIBD0A15XgGM7awkFwudvbG0sUoptAhgjYcK9YIhUslx7SigIDQUdYnlLeef5xqjjQvlYSBjeMaT3AkCnUeENhLAYNDwwK4shiRGnLpQ4EiY/APvfgvRzCCyXMloDLHpUEdC1vesMPV8aXD6eWVdEYZ0LgJk5sdw6MRW9pncv0MPhMC+Vl6AKwcL8o1jCMhFXKAUDZVkm7HIRdjoJOV2EXU7CLlf7/ZDTSdjlJuxsJeRyoc/IIPqrX213iEqShP3uL1P3+BNEHjnKr+OieHjQILzaQfy7spE4cwqqQBl/zBpHxDvLad6zFwDDiOHYv/zlbgloYZ8Pz5YtOFetJlh7fBSIJGEcMwbrvLnoMjMvXEFOcEbUNhtFo2IJVoUYtauJqfecJrf7CxTv3sGB1Z8RDHR0cOpNZky2SEy2SEbMW4TRojh//d42VGoNzdWVHNqwBkd5KQAxllgm500jLSKtfRsfFn1IW7CNKzOvZFTsKD4p+YTVZas55DjEkcYjzE6dzeL0xYzISeF/1Q42NLn4pKGFna1u7kmOYaS15x1esizz6pFX2V6zveM4W4qJPl7kWW8yU1lfD0Cc1cK0aV0ommuJBWI7HocCp0R3SSoVUXfdhaTT8eC6zfzHG02j287vPzrC1OwYjtY6KW5wf8FR7iYmNYG8hAhyE6zErT6AKX+bIpTrdOhzspTOuZwcJaP8pFoFF+x3tOYArH1MeY0SR8PkbyjP662KeH7wHdj/ptJhf9IxmCdNYt7yz9jpC7DnQAl/QKKlLUCTx48vcAZRPBREpe0YqaZvqMXa1orN7yairZUInxvbcVHcbtWT9/VftzvG6459is91DACVyYg6Mgp1lDJpou0YtCquGJHInEEWPnjoL6w1pdIYk8gbHj8f7qtm/pB45g+NR3+pGdCPLIdd/wWOd+jEDVEE88RR3Tc6jLhJEcr3vQEFHyujNqZ9W/ksCAQCgUBwGSCEc4FAIOgHDm2qIhQIE5VgJjknsmcbaa1WCpxV7oTZP+0o/pkxs2fb87Z0COXV+yDgOWmmpAzLPhHBEpl2QcWbyLJyMShJEha9hm/NG4xOozrrcHoyZkLqZJHTCRecaL69pJEXN5XQ5g9h1Kn58tR0JqTbz71iD9jrbMNoyiSu4RhX1NShy8hAn5eH7PdT+9jvCVRVdXlbbXv30fTqa0R96c52AUyXmor97rtxPPMMOeu28bM7h/CSSo0E3DMojYXNRpr//A/amppAoyby+uuxzJnTZQEt2NSEa81a3OvXE/Yo31nJaMAyfTqW2bPRREefYwuCC5lmbzPLUxuYbtSQHYjEu2Ez2rlzzrpOOBwiGPBjtNoI+H0EfV58Hjc+j5um6kpGL+yIODmyYS2FO7e2P1apNWSMGU/ulOnoTR3Fk1t8LXxe+jkhOcSO2h1clXkVV2dezdSkqSw7uoyDDQdZVbaKbTXbuCbzGu5LmcyUSAvPVzbQ4A/yeHEN0yIt3JkUjaWbnV+yLPNmwZtsrtqMhESyJZm6ujoKWwoZn6Q4nD0eDweLSsiOtTM6L4eYmJhzbPUL1BfAxj/D5K8rncEnIUkSkbfeii4tjV+NGMOfVhVS0dTGiv1VilDuUgTd9JZqMrwOMkJOht32I3RJSQC0+SYQSEtCnzMY3aBB3S7qO+BU74N1f1BE86QxMP17nX83c6+A/BXQVKIUkjypiKSk0ZC5ZB5j3trMrnotR6rsSCoVodYWwl4fppAfW7CNCL+HiLYWInxuokxaBn/nG0SbdUSadDQ/9Rd8lQWdmqSI4pGo7dFo1R3nyqhbbgaVCnVUFCrDmX/XpOpKpuJggqOKPS121lozaIpN5n1vgE8P1TA9yw4X86AcWVberxPv04kRZcnjYei1EJvT821LEgy/AWypsPmvyijET34Gs36kFBkXCAQCgeAS5yL7JycQCAQXH45KF9VHm0GSGDYzufsOM78bDrytOH3CQWWIbUtFh3DeE2oOwNrfd45g0VsVZ1nSGMWhfYG6iU7kmWfFWZiTqxQfTYrsogP+5Iv/qj2g1kH80DMufkkhy0pRN13vxp6cD75giNe3l7M2X3GOZsaauX9W1tk7QM4DWZZ5r74Zs2RgQVEVtlCYiCuWIEkSzo0b20VzldmMymJBbbWgslhRWS2oLRZUVuvx562EWlppfPFF3Js2oU1JwXqSuGkaO4bAlVfSunw5ea+9zA+/8S0i09NIbG2m5s9/AVlGExdH9FfvRTdoUJfa7i8pwblyJZ5duyEUAkATG4NlzlzMU6ecVTQSXDwsL16OVx2mdfZoIrZ7aV2xAuOI4WhiO5zSPo+HNmcrkfEJAGSMGY/ebCE5dyiSJBHwenG3NONpaabN2YLW0HF+bHMpxWJVajVpI8eSO2U6Ruupo3RsehtfH/11lh1dRpWrijfy32B9xXpuzLmRB0c9yEHHQd4ueJtaTy2vHHmF9ZXKvN8NzmBZbROfNLSwsdnFflcbdyZFM9lm7tJvnyzLvHvsXdZVrENC4s6hdyKHZXYX7aaopah9ua1bt1Ld1EJ2rJ3hWeeOsjmFwlXgccD6Pyod0V8QFiVJwjx1KgA/XJzHKxuLcK1cSXpLFZkhJ3bZpzjK9Xr0udnt30kA44jhGEd0FuMvGqr3KoW0QwFlhNmM751aUNsQAYMXweH3oeCTTsI5gHnyZG7cso3ctGQiR6UTHWEk9PKLGIuOoONU17kqYCQpzoykVjpYLHNmY5o0EU1Uh3v8TOc3bXJylw7LkJdH0qOP4lq3jokrVzG2aQcHWgtZY0mnNjaFz7x+6hpU7HrnIKl2M4mRBpIjjSRFGkm0GS7cSBdZhoodcOhdJYZl4n3K8/ZMuObp46MseonUiWB5WOlUcdXCpz9XCq7HDFDRWoFAIBAI+gkhnAsEAkEfEg7LHFyviHGDhtqxxXYz4sTvho9+pGSOgyJqj/kS2Lp2sXha6o50iOa2FEidpGzXnqUUirqAaXD5+OuqY5Q3ethR0sS4tCgiDD0opFZzANb+Qcm9XfBriOyaeHlR01KhfJbihsDcXwz4CIKKJg//XFtIdbMXSYIlwxO5dnQSml7IRj4TB5xtFHp8qJqbmFVyFG1qKobhw5GDQZyffgZA1G23Ypk1q0vbC7ucNL+1jOY330SbmIBhSEdufMRVVxKoqqJt926in/838T/9KZr4eMwzpiP7/UTdeus5xW45FKJt7z6cKz/HX9ghGuoHD8Y6by6GkSORLvDvrKDrVLoq2VK1BYDp130DbfnLBGtqqfnNw0RcsQTLvHmUHdrPwXWr0Or1zLv3QdQaLSqVmpS8Ye3b0RoMRBoS2oX1k5l47Y0EfF4kJLTn+Pzl2nP58YQfs7FqIx8WfUi1u5qndz/N6LjR3DD4Bn466aesq1jHiuIVlDvLeXLnk4yLH8fS7KVMikziuYoGKrx+/l5WxyaribuTY4jWnf3SY3nxclaWrQTglrxbmJQ4iTpnHQAVrgq8QS8GjYENGzZQ2dzKpmOl/PhL93brdQZg/FfAWQX1+bD6EaWIdPyw0y5q0Wu4qW4XLsc+JIMBfd5g9DlKXQTdoEHtgm+fI8sQ9HUeMVR7CFw1yn8Fv1vJcPe7lTguW7ISz6G3dH0fRz9VRPPkcUqm+RdF8xMMuUoR0LMXnDJL0mpJ++H3STvpudZRuQTjIxUh3B6FOjKyXRhXGTv/LzKNGdP19nYDlclExOLFWObOxb1hI2M++4wRTbspaCtha8YN1DWAw+2jsbWNvRoNJ/9CRlt0JEUaSbIpYnp2nIUE2wB2VoaCULpByTBvrVSea61S/h+e+Hz0pmh+gqg0WPRbWP8nZZSiLbX39yEQCAQCwQWGEM4FAoGgDyk74MDpaENr0JA7+VQR45wc/lARzU3RipMo6TwvKBuOwZrfKaJ54iiY+cMzXxhfYByubuWfawtxeYPteeY9Es0BYnIgJvu4aPI7WPgImC/xiIvaAyCHQVINqGguyzJrCup5fVs5gVAYm1HLV2dkMjTpNNn0vcyvD66hlShuzk4j4567UZlNSJKEa/MWQk1NqG02zFOmdHl7lnnzCFRW4t68BcezzxL345+gjVdGQbTnndfVobJY2nPQo2699Zxid9jjwb1pE87Vqwk5GpUnNWpM48Yr+eVddKkLLi7eO/YeMjJj4saQac8m+I1v0Pjy//Dl59PwxpscfelFagcl4LdHYbRG4HW5MEd2r9C0JEnoDF3vwFWr1MxMmcm4+HGsKF7Buop1HHIc4sbBN6JRaZg7aC4TEibwQeEHbK7azM7anexv2M+CtAX8MmMenzS6ea+umT1ODz8pqOCWRDvz7NbTus8/LfmUj4s/BuCGwTegUWkIhoNsq92GI+zA4DNQ0lpCnj2P9evX4/EHOFJTz6wFi7r1GgDK6KPZ/w/WP6FET6z5nVJE+gy/saaJkzBPnow2NbX/hPJQABzHoO6QIpA3FCjn7ptf6ljm0HtQvef069fsgxEnFcKu3AkqLcTmKp3Gp2Pqt5VCoEOuAfVZLhMNNhhydZcPJWLhwi4v29eodDqsc+dgmTkD99atTGhqZtaikbz9fgXDJw3m8F+fo8YPDdZoavU23FojdU499Y0G9ul0ykgDCR6cncW4tL6JFDsjQZ8yWuLwh0rBTlBqzgxepGTP90cMm8GmdL77nRdc7JtAIBAIBH2BEM4FAoGgD/E4/SBJ5EyMR2fo5inX54T85cr9cXefv2jeWAyrH4WgV3HWzfj+RSOa7y5r4m+rC5FlmUHRJr4xJ/v84jw0OqXT4LNfKW6tPS8rxa4uZWoOKLfxAxch4PYFeWFTCbtKmwAYnmzj3hkZPe8A6QafVuVT2BZERR1zowZjGqR8n+RgkNZPFLHOunABkq7rGfiSJBF1++0EauvwFxXR8I+/E/+jH6EyKXE4Kr2e2G9/C5XJ1J5zfDbRPOzz0frhh7jWrUf2+ZRtmM1YZs7AMmsW6sjInhy64CLgSOMRDjkOoZbUXJN1DQCa2FgiH7yf/Beew/3BctR+P3EOB9apU8m89ia03RTNzwez1sxNOTcxPWk6la5KIg2R7fNq3DXclncbM5Jn8ObRNylqLmJ50XI2V23musHX8XD2UP5T6eCox8uLlQ1sanLx1ZQYkgwd37VVZat4v/B9AK7OuppmXzPLji7jQMMBDCoDEhKeoIfC5kKyI7LZvHkzAMnJyaSnp/fsoLQGmPkj2PCkktW97gnldyB14imL6jMzeraPnnBsJZRuVITy0GmCt8MhpVgjQHS2ouLqzMcni3Kr1imO4JPjyfa+Bs1loNJAdJbyWxA3VInvispQtqPRwfDru9deWQZfqyKoXkRIGg2W40VlA4EABg1kR+qIcJVCMAQeZZSPGw21KiN1KgOOxHTqR02mpMHNC5tKyYixYDf3Y92Uwx8oRVlBeb1zr4DBC5T3vD9Ra5Si8Sdoa1aK1QsEAoFAcAkihHOBQCDoQ4ZOSyI5J4qI6B64cnQWZah06WZImXB+DWkuh1WPKBfSMTkw80c0+yV04SCmcwydH2gqmjw8u74IWZaZmGHnnmkZ6DS9EE+ht8LUb8LHP4XybdDW1PlC8FIiHIK6g8r9hBED0oRjdU7+tbaIRrcftUrihnEpLBwa3/3M/x7yTEk+yDKTrRoyrR2FBD3btxNqcKCyWjHPmNHt7UpaLTH3f43ax35PsKYWx3P/IeYbX28XyNURXXPS+8vLcfz7OYK1tQBoEhOwzp2LadIkVN0Q8wUXH2E5zDtH3wFgRsoMYk1KxEJ9aTE7PnwHr8uJNGU8aW4/UQ1NqGvqqH34YWxXX4Nl9qx+jetJtCSSaElsf5zfmM/Tu58mw5bBjTk38t2x32Vn7U7ePfYujd5Gntv/HIOjBvOVwTdw2BfN6zWNHPV4+dnRSm6Ij+LKWBsbqzby9tG3AViSsYRAONAe15Jnz0Mtq9GhwxNQhPO9TXtxuVwAzJo2jZI9OwkGAwye0PXRIu1odEon8uanoWyLIlqnTOifUTlBPziOQt1hxeF9QuRuKYfa4+drfYRShyNuqBKzZYlXRg2dYORNXdtXOKyI4363ku1en69MLFPm510FY+7s/nE3FsOWfxyPPfvNgEeAnS8qvZ6Uv/yFYEMDgcoqgjXVmKqriayqJrO2BnN6LhFL8vjdR0coaXDz7PoifrgwF5Wqj47b03g8EuV4Ec7BC6Bss+Iwz5w98MXOQwHY9SIUr4Mlj4M1fmDbIxAIBAJBH3BhqyUCgUBwCdDtXPMTSJLiMj9fp3lrFax6WMk+tWfB7J9wuMHPk58VADAi2caDs7P6NFv6fNhV1owvECYv0cq90zN6t532TKWwVcNRZfjz8Bt6b9sXEo1FSmFQ7XFnYT8SDsss31/Ne3uqkGWZuAg9X5uZRUZM/znk1tYWUugFc6uPez7bhDtkxzxlCnI4TOvHnwBgnT+/xwK12mYj5sEHqHv8CbwHD9LyzjtE3tC1z5Isy7hWr6H57WUQDKG22Yi643YMI0b0W6eCYGDZVrONSlclRo2RJRlL2p+3xcWTnDeMuuJCRsxbRHxGFv7SUppeeRV/aSnNb7yBe/Nmom6/DX3Gub/XciBAsLGJUKMDSW9Al5F+3p+xJl8TOrWO4pZiHt/+OJMTJ3N11tX8Ysov+Kz0M1aWruRo01F+v+33TE+ezi8zFvNGXRt7nR5er2mkrGkXhyoU0Xx+2nxUkorlRcpIqxtzbmR68nTqnHXoJT3NoWaKWooI7esoxDl5/Dj2fLocndFE9vjJPTsetUaJKLFnKoJkX33vgj7lt6bukDI1HFUKfoMiip/IWE+bDtYkRTCPSO6d9qhUMOXrijvcVasI87UHlXa0NSliuix3f18GmzJqKxxUtpdwkRZFPQlJrUYbH482Ph7o+P8lh0LIfj8qtYqvzcjgF8+v43BzMyuSIrhqZFLvNsJZo7jLi9YoZof5v1KeN9jgiicunA4KlQZaKpXP9rZ/XRD1UwQCgUAg6G2EcC4QCAS9jCzLHNlczaCh0ZgjexgnEgr0ToyKsxZWPgzeFohMgzk/pc6n5h9rCgiFZQBcvmAnMbq4wU1qlPGCEdKvGZVEjFnHyNTIvmnT4IWKgHFspVJITdVP+bX9Sc1+5TZ+WL8WgG32+HlmXRH5NU4AJmdGc+fkNIy6/nmNw+Ew7254ieeaqsA8jOlFJcQ3t7bPb9u5k2BtrRKHMmvmee1LN2gQ9i/fhePfz+H87HO0ycmYJ08+6zohl5um/75E2959ABhGjsB+112oLd0o5ie4qAmEAnxY+CEAC9MXYtZ2dCjpjCZGzFmAPHs+quO52rq0NOJ+/CPcGzbQ8u67BMrLqfvD45inT8N21VWEfT5CDgdBRyMhRwPBBgfBRgehBgehlpZO+zaOGknkrbeiier5SJvJiZMZYh/Ce8feY1vNNrZUb2F33W6WZCxhUfoipiRO4d1j77K7bjfrK9ezs3YnSzKWkBU/gpcKN/NK0TtkGfVclTEXq9bKO8cU5/212dcyO3U2AFGGKCJUEbTSSrOvmaJdHYVyp8+fT/mqj/G3eXA6GoiI6WFBRJUKhl7b8ViWFSd4/NCevjSdKdkIW/7eIZSfwBiliOaak0alxWQrU18gSWBNUKbsecpxepvBENkzwdNkV7ZT8AkceOuSEM7PhKRWIx0vZGotOMAVx9bzhjGbd7aaGJIYQVZsL5y3G4vh8PvKSEOU/2iEg8c7vo+bMC4kYVqSYNL9sOIHSsdJ4UrInj/QrRIIBAKBoFcRwrlAIBD0MhX5TRTtrqf8cBPz7hqCWttNodJZC5/+XCn0NOy6nl8kuR2w6jfQ1qi41ub+DK/KxNMrD+P2BUmPMfOlyWmEZLl9FZcvyG9XHMasUzM+3c7kTDtZsZYBcb7Ksty+36nZMedY+jxInawM0U+b1nf7GGhqj+eb92NMiyzL/Pnzo5Q3etBrVdw5Ka1v38cv4Al4+NuGJ1lzdB2VRjMGfxTXlpajjrZjmjABWZZp/egjAKzz5qIynH+RM9P48QSqqmhd8RGNL7+MJi7+jNnI3vwCGp9/nlBzM2jURF5/A5Y5s4XL/DJjVfkqmn3NRBmimJ0yG4BQMIBKrUGSJCSVii9+IiSVCsvMmRhHj6b57bfxbNmKe/0G3Os3nHN/kk6HOtpOsL6etr378OYXEHndUswzZ/b4s2fT27hr2F3MSJnBWwVvUdpayrvH3uWQ4xDfGvst7h1xLwVNBbxV8BZVriqWHV1GnGk9waZKZBmadcPRaxN459jrAFyRcQUL0hZ02ke8Op5GbSOegIc95XuU/dpsjBw5CteR/TSUldBYUdZz4fyL7H8TDiyDkbd0PfM74IWGfKWQZ90hpVM243j8U0SiIoAa7cejV4ZA3DBFwB7I77wknX9E2ZBrlY7nusPKsfdWZ8MFjHHsWCavXkNBZT37ygz8a42Zh64dfn7RdztfgPyPOh4njoZhS5XPyoWMNQFG3gq7/wu7X4bEMZd+sXWBQCAQXFYI4VwgEAh6kYAvxJHN1QBkjYntvmgOygW7rxXqj/T8gtrTCCt/De4GJRN17s+R9RE8u+oYVc1t2Exavjknm6gvFLWqaWnDrFPj9AZZfaSO1UfqiLbomJQRzaRMOylRpp61p5usK6hnW3EjD8zOwqLv458qjQ5m/ahv9zHQpE9Xiof1o3Be3OCmvNGDVq3il1cNI8F2/sJ0V6lwVvDv/f+mPlxPo1b5jEfXbiHFbyJi8WIkjQbPrt0EqqqRjAYss2f32r4jrr6aQFUVbXv24vjXP4n7yU86OXrlUIjWFStoXfERyDKa+Hiiv3ovutTUXmuD4OLA6XfyacmnAFydeTXa46OMdn/8IV6Xk5HzFxMRE3fG9dUREUTffTfmqVNpfu015fOs06GJiUYdHY0muuNWY7ejjolBZTYjSRKBykoaX/4f/uJiml59Dfe2bdjvvBNtYuIZ93cuMmwZ/GD8D9has5X3j73P1KSp7fNyonL48YQfs6lqEx8UfUCdp45UvQarZTiqyMV85PSgUxmYmzqjU1zNCeLUcZRqSmn2NNNmagNg6tSpqNVqolMG0VBWgqOynPTR43rc/k6cyBHf97oSQzHq1lN/j0MBpVOy7rDitm0sBrkjRgZrYodwHpkOV//leEb5JdY5Zo5W8raPfa50NlyMwrmrjhjnQaQSi1I0VqVROjsiBynzQwEl8kylAZUaSR9BzL1f4fpHf0epy0JNUQX/2xLBfTMze7b/2kPHRXMJBk1WRj/Y+zdW7bzIvULJXnccg+3/Vv5TXWqfc4FAIBBctgyocL5u3Toef/xxdu7cSXV1Ne+88w5Lly5tny/LMr/+9a955plnaGpqYtKkSfztb39j2DAlA7CxsZFf/epXfPrpp5SXlxMTE8PSpUt5+OGHsdk6Krs3NTXxrW99i/fffx+Aa665hqeffprIyMj2ZcrKyvjGN77BqlWrMBqN3H777TzxxBPoREEugUDQDY7uqMXvCWKO1JMxqgfu2pYKKDnuGhx5S88a4W1RCoG6asEcA/N+BSY7u8ua2FPejEYt8Y3TiOYA2XFW/njzaA5Xt7KlyMGusiYcLj8r9lezYn81907P6HPX8NFaJy9vKSUUltlc6GDBUFFs6rzJmqtM/cjGQgcA49Ki+lU0X7HtTT5uXYdPDlMbMhKZdju+ireQAkXUR40lZfLkzm7zOXNQmXqvQ0iSJOz33EPdHx4nUFmJ45//JPb730el0xFsbMTx3HP4C5WoCfPUqUTecjMqfQ8jnQQXNR8Vf4Qv5CPVmsqEBKUAdHNNNeUHleieUCDQpe0YcnKI/8UvkNvakIzGLjnHtcnJxP3wB7jWrqXl3ffwFxZR8+ijRCxZQsSiRUianl0iSJLE5MTJjIkbg07V8RuzpXoLFc4Krsi4grHxY1lZthIJickpC/l1YQ0tQTNDUr7O1Zmnz12PU8WRak2lobEBQ7JyPplxvJhvdLLS6eSoLO9Rm0/LiBuVgpe7X4ZD70LQCyNvBp9TcdiCUrRxzWOd1zPHKE7yuCEQf1JsiUrVsd6lyNClSh537QGl6Ghs7kC3qGs0lcKh91CXbCCrrhbV1t0dcWZ5V8HYLyn325rhs192rCep0Mz5GUl33MZtz73KP+t0bNxvYVhyBFOzevAfyZ6hvIYhP4z78vkeVf+jUsGkB+Djn0DVLijdqHTYCwQCgUBwCTCgwrnb7WbUqFHcc8893HCaIlp/+MMf+NOf/sQLL7xATk4OjzzyCAsWLCA/Px+r1UpVVRVVVVU88cQTDB06lNLSUh544AGqqqp466232rdz++23U1FRwccffwzA1772Nb70pS/xwQcfABAKhbjyyiuJjY1lw4YNOBwOvvzlLyPLMk8//XT/vBgCgeCix9nopWRfAwBDpyeh6kke977XARlSJkB0VvfX9zkV0by1EkzRimh+fMjsmNRIbp80CKNOfdYsTrVKYniyjeHJNvzBMHsrmtla5OBgVSvDkjs6JfeUN9Pk9jMuPYoIQy/ksQMOl4+/rT5GKCwzLj2K+UPO7LbsdbytSoFQUIZHC3qMPxhmW3EjAFOz+2fItj/g4+8rHmN9zUa8RgNywiQi46/FrjZh37cMTyjAmowgY7Va2vbtI1BejqTXY5k7r9fbotLrifn6g9T+7jH8pWU0/fe/GMeMpenl/xL2tCEZDdhvvx3ThAm9vm/BxUGtu5YNlUon6dLspUiShCzLHFjzGQApQ0cQlZjc5e1JkoTUzQ4gSaXCOmcOxlGjaHrlVbwHDtD6wYe07dxJ1J13os/soXsW0Ks7OoP8IT/vHnsXl9/F9prtXJ11NVdlXsWBhgOEgk18Jz2eRwurOdwm8259C9fHnxodEqWO4pEpj7Bo1yJUJhUam4bp0xVhzp6UApKEu6mRNpcTo8Xa43Z3YsjVoNbDjueg4GNlih8G844LqAabUrzbYOuIXrH0UlTMxYYlFjJmKRnXJRsuDuHc0wgf/RiQQZZxGpKITRgKkgzhkDI64GQsccrzgTal02TbM5iueIKhkw8yf3sJn5XreHmDiexYC3ER3ews1hph9G29dmgDQmSqEmt0ZIXizBcIBAKB4BJhQH/VlixZwpIlpw7HhOPZqH/+Mz/72c+4/nolW/DFF18kPj6eV155hfvvv5/hw4ezbNmy9nWysrJ49NFHufPOOwkGg2g0Gg4fPszHH3/Mli1bmDRpEgDPPvssU6ZMIT8/n9zcXD799FMOHTpEeXk5SUlKVfQ//vGP3H333Tz66KNERET08SshEAguBkLBMP62ID5PEF9bkIhoA0ar4qhrrHKzd2UZclgmPiOCuLQenDcai6B8GyD1zG3eVKoUH2suUwp9zf25cqF3HEmSmDeke+5tnUbFhHQ7E9Lt+IIh9JqOoo6fHKyhoMbJ/7aWMSwpgkmZdsYOisKg7VnhR18wxNOrjuH0Bkm1m/jKtIz+zXtuLIS9r4LWBDmLleHalwKlmyEqTYkN6KfXc29FMx5fkCizjiEJff8bWtdYxWPLf05JWwX1Wg0u0xhiLXPIMEdyr7+VfSUaXkyV2EEJLa4mvMtXAGCZNQu1xXyOrfcMTXQ00V+7j/q/PIVn+w4823cAoMvIIPor96CJvUwFNgEA7xe+T1gOMyxmGLl2RWSsLTpGfWkxKrWaYTP7b4SIxm4n5htfp23HDpreeJNAVTV1jz+BZfZsbNdec975/zq1jruH3c1bBW9R467htSOvsaZ8DfWeeoxaIz8Y/wO+khzDMxX1vFPbRKpBxwTbqd9LrVpLw9EGMII5zcyE4x1PWoMBW2w8LfW1tNbV9p5wDpCzUInz2vJPQFY6WGW541w6+ye9t6+LnWHXQfI4SB470C05PbKs/D+KSlMem+xKLAoyoZwrObT5MOmzrkCtPY0RwBIL1xw3U/k9sPz7yqi+A28RecstLDj6W446G6lsbuWZdUX8ZEle14qZB/1KIfhLJdZkyLVKcVCD7dzLCgQCgUBwkXDBdgcXFxdTU1PDwoUL25/T6/XMmjWLTZs2cf/99592vZaWFiIiItAcH2K6efNmbDZbu2gOMHnyZGw2G5s2bSI3N5fNmzczfPjwdtEcYNGiRfh8Pnbu3MmcOXP66CgFAsFAIssywUAYv6dDDLfFGjFFKGK4o8pF/uYafN4gfk+QoD/Uaf2Rc1JJHWoHFFHd0+pHpVExZFrSKfvqEnuVwmikT1OcO13F2wL73lCKcyGD3qqI5hFJtLQFeGtnBbdNTD2/olXQSTSXZZmxg6LwB8OUNLg5UNnCgcoWXlKXMnpQJFMyoxmVGtnlbcuyzPMbSyhv9GAxaPjm3OweC/A9JnG04jBz1SrDjLN734nc7/hcsPEvgAxL/6EIBf3AxmPKyIupWdGoVH0rCOwq2MRT6/6AI+yhVm8lEDULLOkMM7r4VXYSOlUKSd97nBXL/w+H7GHlW08zobQaSavFumB+n7bNkJND1K230PS/VwCwLlqE7eqrehyDIbg0KGwuZG/9XiQkrsu+DoBwOMSB1YrbPGvcJEy2yH5tkyRJmCZMQD9kKC3L3sK9eQuu1atp27OHqNtvwzji/Ooj5Nnz+MnEn7ChcgPLi5ZT464BIDcqF7vBzgyjijKvn48bWvhneR1xuiTSjJ0jjGpqaqg5WINtso3BkwdjOEnQH3/19RgtVrS9UOT3FDJnQ9xQkNSi6OHZsMRemI57WVZMCYfeVXLor/5zR2zO1G8pMSOBAHC4a9vTmWDCvbDucTj8Aaq0qcTedy/fbG3j0X1uihvcvL+3iuvHppx7W3tfhYYCmPDViyvT/EyoNaA+STQ/3skUCsuEZRltT0ZifgGXL0hJg5sSh5uyRg8GjZqMGDPpMWZSo4xd67AQCAQCgaAbXLBXbjU1yh/q+PjO7sj4+HhKS0tPu47D4eDhhx/uJKrX1NQQF3fqUP+4uLj2fdTU1Jyyn6ioKHQ6Xfsyp8Pn8+Hz+doft7a2AhAIBAh0MZfyYubEMV4OxyoYGHryGZNlmYA3hK9NEbv93pAihts6nOH5W2rb54fDcqf1h89MImWIMkzc7wvgqHZ1mq9SSeiMGnRGNbIUbm+bKVLD8DlJ2GKN6Eyq7n8vnDWoq/YoFxh5S49fxJ2DUADp6CeoDr2jDB0G5NTJhEfdBuZYAl4fT688RmG9i1aPj2/O6UH0y1mYPdjO7MF2alq9bCtuYltJE3VOL1uLGnB7/QxN6HAMyrJ8Vvf4Jwdr2VrUgFqS+Nr0NGz6HryGvYCUMRfV3v8h539MeNCMfnGB9eW5VKrahyocQo5IIqy1du1zdZ40ewLsr2gmLMtMSLP12fsYDod5Z+NLLDv6Fk0qiUZTEuHouRgMEXxnUArXSFFIoRCBUAh1YgKLR97Ae5XLCa/fTiichGXaVMIGA+E+fk30kycTabWiMpvRpacTlOV+eR++iPjNvjCQZZll+csIh8NMSZxCtC6aQCBA6b7dtDTUoTMYyRw/eeDeJ70O6+23oxs7lpZXXyXgcFD3179iHDeOiBtvRG09Pzf3tIRpjIoexedln6OSVFyRfgWhYIgQIW6IsVLqaeOQy8ufiqv5ZUYCERp1+2vxwboPiJwaiS5BR2xSbKfXyHi8o6HPXjf98fgY8f3pGifiTEwD0NEgy+BthoAXyXEU6cgHSK2Vyjy1jnD9UWTDSe0Khbp/fowfhSplIrLehqy3I1mMxCTCnYYm/rW+mA/3VpHz/9k7z/A4qrMN37O9SdpV7724925sXLAxpvcWSkILJIFAgJB8aZAECJBQEgghIYRqqkloNjZuGBv3XmX1Xna1vZf5fowtW1hylW3Znvu69trRmTNnzqxmZ2ee857nTTFQln6I74urCeXueRCLEvXaIe4IhPbTCKFhLcKO/+IY9xB//KoBhy9MolFDapyW1HgtaXHazuVko6ZbwdsXilDX4afG5qPW5qO2w4fVE+xaSYTle6RFlUJBtkVPfpKB/CQDBclG0uK0vTeAH4uApx3B0wzuFgRPC/jtiCn9EYtnSLMH+jjHey8g30PIyMicjfRZ4Xwf3xVaehJfXC4XF154IQMGDOC3v/3tIdvorp0jqfNdnnjiCR599NGDyhcsWIChFxON9XUWLlx4qrsgc4ZzuHNMFMHXoCLYoSIWAcSu31tTXghdihQtHnIpcJV3jWITlCIKtYiggrXrG9hSLdWNhTcZN0QAAQAASURBVCFsUqJQ7VsvIighIoAP2Fi+m43l3+nMnmM/Tp1mFnGBJtqXbzh0RVHE4qsk1/Y1urADAK82ldqkKbgd2bBsLaIIX7cI7HYKaBSQaGzhiy92H3vnDoMKGK+Cdj1UugSU7S188YX04XjC8L9aBUXxIsXxIknag/XojiD47QqGJopUrG+m4oT19NCoogGGt3egaGtju+dVPLpjnD1wDJyIa2m+dTFpzjZag5nUfPFFr7ffHZttAi3tAml62PBNz4PPx0NEjLAq8C07bBuwaXX4DcWEE8aSGglzc5ubrI//w/aGRtpnX0B4ryWKKBqwNIZQ1rRQo47iFkViJ+kz6WTHjpO7v27o67/ZATHAzvBOaiI15CnzGKwZjFro+2LEkVIRrmBNcA0qQYXKo+KLSukctG1aS9DRQUJxPxYuXnyKeykhjBhB3KZNmLbvgHnziC1ejHP0aPxFRcc8qCiKIt988w3vv/8+oihS/7N6Cgr2R9oWIbBBZ2GHoORndbVcEnSwb+7RF+99QawwhqAUCKgDzP18LjrhDLHUOoMwe6soap+PW5dJefplh6wriFGUsRCKWBilGEIZC6OIhVCKIUKqeLxaKbBJGQuS3bFSWr+3njIWQiFK73ZjEXVJ50p1o0FG1bzYZT8RhZbWhGG0GIYT2doBW7u/9h/V9VHc+z1oWdKlONkRYE9zgMdsLVxZKKLrYfJcWfNczL5m7MZCyjfUA72Y3PYUI4hRBte/gT5s56Xtb7BbLEUIhWmPRtmlUiGqlF2uIQIQp4YEDSRoRAJRaA8IOEMHtBmNIoTCCLEYJrOBZJ1Isg40m7bSFtPQaEjCr9HTrNGwRq2GvWK5WoBkHaToRVJ0Iik6aV89XsLEGNqIC13YgU+TTFgl5QRKdm+nsH0Bgih2s9E8qjdspi1haO98gCeBY70X8Pl8vdwTGRkZmb5PnxXO09OlKXQtLS1kZGR0lre1tR0UHe52u5k1axYmk4mPP/4Y9QHedOnp6bS2th7Ufnt7e2c76enprF69ust6u91OOBw+aF8H8otf/IIHHnig82+Xy0VOTg4zZ848K3zRw+EwCxcuZMaMGV0+cxmZ3uJIz7HmCiebGxqIO8CFQq1VotWr0OhV5A5KJL1Q+k6GAhEcLX40eiVagwqNToVSfRpN63TUodj4JoJnO1g0oC8lNvhakvInk3vAU8CiXW3Y7Q2kpQrcO62IgZmn7pq0cEcbRncDLUCLD9JVOsbkWxhTkEhq3P5BjKvDUbQn256lGxRrrAjVy0jJDBIbN/uE7+9EXksV8xYjaFNJnngtA7JPfCJKURRZ89kuUgU/N47NZXJJcq/voyPQwavbXqXd6cGpziZIGULCAK5LS+A+UnC/+RbRYBAhPZ3SsjIM48Z1bpv22CJc2jZaR6RzxVVX9Xrf+jJ9/TfbG/aypH4JqxpXEYwGMWCgnXbWa9dzSeEljEwdeXJzHvQyrb5WPqn8hEpbJamkMit/Fhfk78/zEzv/fBp2biO7/yAUylN/Hezk0ksJ1dXhfOcdwg0NpJeXoxUUJFx/Harko/t+r1+/np/97GesXLmys+zxxx9n4cKFDBgwoLNsXDDM76tbCERjdCT244bkOBYuXEhTQxNBfRBdto7k3GSKxhQxNGW/UFW5dhXNFbvpf84UknLyjv/YZY4NVzPK+d+C6KHUUilF6YYDEPEj5oxDLNv7u+q1ovzs3h6bEbOnERu9t27Ig/LjD3qoqSEnN5tB4/fWjUVRfvgRqHWI2njEgimIxeeRpjYwpIcWjvv6KIrS8QkaBj36e54PZ2EPmWmN78cPJx+cr0Vo2ojC7YP4TJJmPUpxXEYPDZ/GWMvY+OlLNLQbyUjWc5vKR3DdWqwKHTaFjg5TIjZ9AjaVkahGgzLeTFChoC0aAkFErYyQiB9zyEOm20qmz0pOzEuOSUn+T34PSPccLRu/QgwHEZ0VdAhaGpRGGlTxNMan0WJKJpaSRhSke9AI4AGTVkV+koE8U4yCaC35yjbMwSYpitzXLp2zQGzI3Yj5k6Tjac5C+fVGUGkRTWlgSkeMSwe1HqFpI0lTHtwfcR4JgKpvDuod77m+b4a9jIyMzNlEnxXOCwoKSE9PZ+HChQwfPhyAUCjEsmXL+NOf/tRZz+Vycf7556PVavnkk0+6+B0CjB8/HqfTyZo1axgzZgwAq1evxul0MmHChM46f/zjH2lubu4U6RcsWIBWq2XkyJE99lGr1aLVag8qV6vVffKh9ERxth2vzMnncOdYTr8kPB0h4pJ0pOTEodEpUfTgcahWqzHG6U9UV48eUQR3C8Qf5qHpuz7mKg30vxgGXIpC3fV4tjc5+XBDEwpBwbWjcxiWd2o9WWcMyiDdYmB1VQeb6x20uUN8trWVz7a2Mr1/GtePyUEQhL5zHel3AdQuh8a1KKO+k5bkqtevpb4O8LSAQokicwichM+32uqlxRVEq1IxvjgFtbp3bzNWbP2Kd6reo1WtoC2iIj37VvRqMz9INzFi3S5c8z4EUUSTnkHS7behydmfKyBYWUlWaxi7KLLA0kJJ1VpGlE3o1f6dDvS132xv2MviusUsqV9CKCqFF+Ym5DImfQxfN3yN1W/l7d1vs6p1FVeXXk32aWZn4A65+aLqC75p+gZRFFEqlUzKmsQFRRegVhzwf1CrKRo+6tR19BCoi4ow/OIXuL9ahOvzzwmXl2N7/AniL7mYuGnTEA4j9Dc3N/PLX/6S//znPweta29v54ILLmDZsmWUlJQAkKdW86O8dJ6taWGZw0uOVo3f72fz5s3EG+PRoCEkhKjz1jEqc/9n5ra142huxNHcSHphca9+BjJHQVIuFEyCmm+gYfV31hXu/y3SmyR/cQCFCtR6UGmlBN0qHSSk70/UqYyHQVfsXa+X1u+rr9KD3nJAUk813PDuMc2KOKbro7sFVr8MSi1MeYTMG67jhpdf50Wrjg07G1mZY2FK2QG2odEIbHlHOvb+F6JIzD3qfp4OuC2lvBOZhkA7FylXMzBtCL7sRCJtbYhBG3Q0gLcNMRLEGRNQzO5PezBKW1hHaJuVdLeb7LhkjEgiNmIUdVY2mrw8VICw9/+U9cc/EKqtJVRTi7G2lrTaWoa766C9DrW5kPDEPGqaWqlpc7JjQx0txixccUlsc/vZLgYQbHUAWFRGCrQpFGi05Ot95CfqMag1+8/XzMFwxT9Abzn43Bp8Jcp9ZbEofPlbKQnt0Bv6pu8/x34v0JfuH2RkZGROFqdUOPd4PFRU7J+QX11dzaZNm0hMTCQ3N5ef/vSnPP7445SUlFBSUsLjjz+OwWDghhtuAKRI85kzZ+Lz+XjrrbdwuVydo6ApKSkolUr69+/PrFmzuOOOO/jHP/4BwJ133slFF11EWVkZADNnzmTAgAHcdNNNPP3003R0dPDggw9yxx13nBWR4zIypyOxaAxRBKVKgSAIDDjWhJynmoZ1sPzPUHKelByqO8q/lBJI7fUxJ3ccDPtetzfjra4ALy+rQhRhQnEyMwb0PGvmZKFWKhiRa2FErgV/KMrGOjurqjvY0eRk6e42ilKMjC3sQwnXkoogpQyMKRAJHb5+X6Vlq/SeWAha00nZ5cpKKSno8FzzcSejPZBYLMYbi/7Kx9Vf0KLRoLaUkZN9I+ekZHGTXsD/2n9wVVUBYJwwAfO116D4zsC264t5qBQqfAVZOBQO/rfxvbNSOO8r7BPMl9YvJRiVPGuz47KZXTCbwcmDEQSBSVmTWFS/iAU1C6h0VPKnNX9iUvYkLiy8EKPaeOgdnGLC0TBL6pewoHYBgUgAgMHJg7m0+FLSjemd9dpqqkjKzkXZxxPGCioV8bPORz9iOPa33yG4ezfOj+biW7uWxJtu6jJItY9AIMBf/vIXHn/8cbxeb2d5WVkZf/jDH3jiiSfYsGEDzc3NTJ8+na+//pr8/HwAhscbuDo9kfdbOni7xY65volYLEawMUicLg5f2Eelo7LL/pKyc2jYsRVbQ90J/SxkjoCRt0JCjiQwqvSg1knvcQfck2hMcOWrkgiuPMz5r1DCsOuPfP8nc3ZKLArWPVKUcu1K9EMmUjZ5NLNW7OaLBjVzVhoZkBlPatze4K7y+eBuBm08DLzi5PXzJDNnTR12v4FcbZiL9FtRYSN+5gDEUb8g5nQSbm0j8tFDRBwhjL4oiepaBI0AKh12UzMBMR7N6OFo8vPQZGejXvsYClMLJBpg96fSvVpiIUqNiL40vzOBsWitILror4Tq6lAIjejWrSMHmOCP0LS9nnB8Ns0JhTQojDSqTTSqlFgNSVi1qdj1CWxQ6UBQg10gbaOOgroq8pONFCQbyE00o+nu3DqwrG0nuJrA1Sglpe13IQy4TEoqKyMjIyNzWnJK79LXrVvH1KlTO//eZ3tyyy238J///IeHH34Yv9/PPffcg91uZ+zYsSxYsIC4vYmJ1q9f32mxUlzcNbKkurq68+b77bff5t5772XmzJkAXHLJJfztb3/rrKtUKvn888+55557mDhxInq9nhtuuIFnnnnmhB27jIzMsRONxtgwvxZRhJEX5KHsIbq8zxOLwZb3AFF6gOyO1h2w7t/SsqUARt4Cqf17bDIUiaFTKchIMHHz+Lw+Z22g1yiZUJzMhOJknP4w25uclKQdX7K5E8J5j57cB+8jxWuDb/4C0ZD0kB6Ldn1PLoUpP5fqGpKkaLz0QSela+FojNVVHQBMLO49ixaX18HTn/6aNe5y2nV6RF0pqvhzuLugiEmWODxLlxKqqkLQ60i88UYMow6O2g3V1BDYvh0UCoZ97ye8v/r3bHftpK6lktz03k2aezbgCDgot5ez276bWlctOqWOJH0SyfrkzvdkfTJmrRmF0PX67Av7WFS3qItgnmXKYnbhbIYkD+lyzVIr1czKn8XY9LHM3TOXjW0b+brha9a1ruPSoksZnzn+oPZPNaIosq51HZ9UfoI9YAcgJy6Hy4ovoyyxrEtdV3sbK95/C6PZwtSb70Ct65tT+w9EnZpKyk/vw7tyJc6PPiJcV0/rE08SN2MGCRfORtBopASoH33EQw89RE1NTee2ZrOZ3/72t/zoRz9CrVYzdepUpk6dytatW6mvr2fatGl8/fXXZGdLswouSkmgPhBiZYeLZXEpoFASbA6SFJeEK+qi2llNMBpEq5QGyZKypMhda30dzrZWElJP/cDxWYs2DgZedug6gnDSBnVPKAlZMPBy2PoBrP8PZAzBfMUVTNv9JLvsHVTX1vG/jcncMblImmXYtDePzbDrz1gxdWOdnZVbaok0NXFhqBJFclQSkpHyiinNZpRmM1xwNQhKiEuH+EzpXRuP+fIgQiQAerPUoKsJFFHw2aRXw9quOxxyrTQjARA0BlTRFlRZGkCQgkziMhE0SSTeHCDkVmFs95DXUI/obwMg6K7Enj8F2+iBVFu9VLW5aK1posmtp6XDy6oqZWffO5OPJhspTDaRadZ1TWyaPghmPQEb34TW7bDjf1C5BAZfDcXTpUEgGRkZGZnTilMqnE+ZMgWx2wQbEoIg8Lvf/Y7f/e53x7T9PhITE3nrrbcOWSc3N5fPPvvssG3JyMicWqKRGOvn1dBe50ahUuC2BjCnnaYPHnUrwVkvTUvud9HB60VRijQHKDgXxt3drZjb4Q0Rp1OhVirISTTw64sHIMakSO++TIJezYSi3vfA7hX6gmgei0oPXQEHFEyWygyJ4LdLD47dEfLsX04fBDoz5J2cqOotDQ68wQhmg4YBGb0zW2tP/Xb+tPBRduPBrYtDMI0hLbmUpwaNYohF2odpyhSiTiemSZNQJXU/c8E1bx4AhjGjyRk2iZLtBezxVPPxmre575Lf9Epfz2S8YS/l9nLKOySxvM3XdlCdGlfNQWVKQUmiPpEknSSmqxQqVjWv6ozA7kkw/y4WnYXbBt/G7o7dfFD+AS3eFubsmsM3jd9wTdk1FCQU9LjtyaTCXsHcirnUuaSIZ7PWzCVFlzA6fXS3x7dt6UIQRRJS0k4L0XwfgiBgmjgR/aBB2N9/H//6Dbi//BL/hg20jRrJfc8+y7JlyzrrKxQK7rrrLh577DGSD/BFT0pKYuHChUyZMoVdu3ZRXV3N9OnTWbZsGenp6QiCwO3ZyWxyerCHIygzsog21jEgcwDrHevxhD3UOGs6ByTiU1JJzS+kraaKVXPfY+ott6PRn6b3B32QcCiIWnOwRaUMUkRx7UpJHN7wJsL4e0i67TYufOoFXnDHs3JjFbOHZJJl1sPUX0H9asgZe6p7fULwBiO8uaKKcEMjk8MtlE2agOKcEsk+LuE7VlvDv9dtG4JaJ81S2Ed8Jlz9GnRUQ0cVdFSCrRI8rYAAQff+usZUOPdhiMuQlvfOZlAAxr0vADESIdzUJNm8VNeQNbwf+kHSbKBgRQVVi+fTqDRSrzDSGJdCkzEJr9ZErUdPnVXP8j3SPbZaqWBsYSKXD8/CbNBIjScWwLRfQ+MGSUB3N8O6V2HPAklUV8p2JzIyMjKnE317XqiMjIzMAUTDMdbNq8Fa70apUjDqwoLTVzSPRiTPcpC8yruLumraCNZy6QZ76HUHibmBcJT521qYv62Fy4ZnMWvvDX+8Tr4h7zUcdVLUf9msk7O/aBhatkjTexvWSUK4xgR5E6UoJUGAST+TyhWqA15K6V31Hf/+C/9y+CnwvcSKCknMH1+UhEJx/AMPC9f9l5c2/pMGjYqwMhHBci7TMvP4jaWY0DsfE7vtNhQ6HYIgYL7ssh7bCTU04N+8BQSB+FnS//GiIVfx7MqnWdWyiu977MSbLMfd3zOJQCRApaOS3fbdlNvLaXQ3IrI/UEFAICc+hzJLGUXmIiKxCDa/DavfijVgxeq30uHvICpGafe10+5r79J+pimT2QWzGZoy9KhmxZQllvHImEdY3rCcz6s/p95dz5/X/ZnR6aPJNp1a7/MqZxWb2zcDoFFqmJk/k2k509AoNd3Wb6uporWqAkGhZOC5009mV3sNZUICyXfcgX/MZppee43Ni76i/rXXyPF6MQgKfGKM6dOn8+yzzzJ4r43Cd0lLS+Orr77i3HPPpbKykvLycqZPn87SpUtJSUlBo1BQqFXh8XhQ5RWSrRKY3W82QrWALWCjylnVKZwLgsDoS65kyev/wue0s+Z/HzHhmhtQyBGex42toZ5Vc99l5OxLSS8uPdXd6XsoVTD2Llj4W6heBgWT0GQPpv/5kxm0YCc77HH8d2MjP5paLHmb540/1T0+Yby3th5rdT3JQTez4gIkXHopdJMT7KhR6yFtgPTaR8i3997ngOusUgVZPeco24egUqHJzUWTmwuTJnVdp9GQOno45poaytqboKMJsQOcgpp6hYmOcVNoSc2jxubF5w+yfGcza6o7mDUonfMHpqNT771fyx4JGUOhcpE0IyG5VBbNZWRkZE5DZOFcRkbmtCASjrL28xo6Gj0o1QpGX1hAUtZpPMW3eqkUKaONh7LZB68Xxb02LkDJ+VKkcecqkW+rbHy4vgGnLwxAeaub8wem9TlrltMaXwd88TAgQuYwaQrxCSLeV4di1YvQsmm/lz1I092zR0tl+wZXko7CWuQkieZOf5gtDU4AJhYfn199NBbl06pPebVyDvVaDYI6HX3yZB4uG8R5Fe04Xv0LRKI4//cJlmuvOWRboiji+vRTAAwjR6BOl/6HEwZO5611/6Y9ZON/q97hpvN+dFx9PlOod9eztH4p61rWERWjXdalG9MpSyyjzFJGsbkYg/rQg5YxMYYz6JTE9L0vd8jNgKQBRy2YH4hKoWJq7lRGpo/kk4pPWNW8irUta1nL2sNvfIIREJiYNZHZhbOJ13Q/6yLmCxO2B9i2ZCEAhcNHYUrsQzkejpJQKMSLCxfyp9deY7pCwWSjifFGI+NTUii65x5m3PsTFIpDz37Kyspi0aJFTJ48mbq6Onbs2MHMmTNZtGgRiYmJqFqbEMUYyvxCJhVmc27OuQiCwPu73z/I51yjNzDuimv5+q1/42hpwtNhIz45tYc9yxwJ1roavv1wDpFwiKqNa0krKpHvNbojpUzKV7NnIaz5J8x+hrjp07lGn8ATdRo27NxDzYAk8tPO3IHabY1Ovt5cS9Rm46pgNWk/vP2gfCO9ygmyutHk5pJ02w8AiHm9hOrqCNXUYKitJammlqQJRWiLihBFka0LV/Dul5tpSMvnf6EIy8rbuXx4FhOLkqUgAqUKSs+H/HNAjJ2Q/srIyMjInFhk4VxGRqbPEwlF2Ti/FnuLF6VayZiLC0jM6NuJ4Q5JJAjb5krLAy/rOh11H3WrwF4jJc0acGlncUWbmzlr6qmxSonWkk1arhmdzYhci/wg29sYEiXBvGmj9CA84qbeazvsB4W6U9hO8Nch1NZIkWh6iySW54yV/OxPg2jJVVU2RFGkMMVIRoL+8Bv0gCfk4R9bX2Vx8zYcGgMa7UCykwfzl5JRGD/4BMe2bQDohw4h/sJuBpwOIOp00vH66wR27AQgbtYFnesUCgXnl8zmre1vsrhmEdeGb0ejPjstCGJijK3WrSypW0KFY3/C9iR9EqWWUsosZZQmlvYoBPeEQlBg0Vmw6CyUWEp6u9vEa+L53oDvMTFrIquaVxGKntpEvnqVnsnZk7sk/vwuwVoXnhWN+GwOcIZRmXSUTZh88jrZi4iiyKeffsrPfvYzKiqk8+Y9YKcg8OTkcxmQmopi1y5sL7+M5frrUVkOLRbm5eWxePFiJk+eTFNTE5s2bWLWrFksXLiQtg3rAFDlFTIxT7J6KUwoBKRI/2gsivKA62RCahpjLrsao9lyWg9K9AXa62r49sN3iIbDpOYXMuayaxBjMRrLd5LVb6B83/Fdht4ADeul/CJ+O4q4NIqnncP4TxbzbWMtH3+4h/vv/lGf/V2v7/ABkJN49IK0PxTlteWVhBsamBBuY+DEYejKyg6/YR9HYTSi698fXf/9+YX2WcUKgkBe0M4PvdvYWtvIfFshtvRcXvOF+GpHK9eMzmFgZoK0keY0fm6RkZGROcuRhXMZGZk+j88VwmULoNIqGXtx4elrz7IPZ4OU3NGQBMUzDl4fi+6PNu93EegkwWr+thY+WFcPgFat4KIhmZzXPw2Nqm97mZ/WlMyQhPOqJTDkGulh+FgJeqBxvWTD0rwJznlAmsYLWE1lxPL7oyiYKE3lPY3ECFEUWVlhBWDCcSQF3VKxhj9++xda4vREBQ3p6VdwU+E4ZlrbcPzlbwScLgS1CvNVV2GcPPmQgo1/23Y6Xn+dmNuNoFZjuf46NNlZXepcMPpKPq/5HDFOy2brFkZnjD7mvp+O+CN+vm36lmUNy7D5bYiIRPwBsvwJ5LYaKEosoDhlFOmpJQiHiRjuTcSYSLDGibe8HWNJCtrChEP+rwsSCvqMx3lPiJEY3vWtBHZ2IIox3DYrPq2bsvGT0RpOv9+zbdu2cf/99/PVV191lgmCwG233cYf/vAHUhMTcc2fj+vLLwls2UpL+R7Ml1922O9tUVERixcv5txzz6W1tZW1a9dy4YUXojUYEM85H4XJRP8J5wBg0pgIRAIEo0GavE3kxOV0aSutsLjL36IoyiLvUdJWU8Wqj94lGgmTWlDMuCuuQaFUseQ/r+Bsa0GhVJJZ2nOy8rMSjQGm/QpMaftnfEVCXBL4lNWUssWfSXm7j9I+lBRdFEV2NLuYt7WFnc0uAM4tS+GqkdkYNEcuFXy4vp722iYsATezDR7MV1xxorp8yjnwWhJ/0UWoMzIY/vHHDLBtYKWvgSUJxdSmZ/EXu59BWQlcMzpH8reXkZGRkTktkYVzGRmZU8r25Y0ICgG1RolKo0ClUe59KTotC+OT9Yy5uACFUsCcevqJDAeRVAQXPSdZtai68b+tWS4lEtKYoN+FncVDshOYu6GBCUVJXD48mwSD7JN4wskYDsZk8FqlxF9FU49u+4ATGtZKYnnLNjjQ/qJtR6dw7temIA6fDerT739a3+Gnwe5HpRQYk594+A264aPlr/O38g+wazQogybG9buDn5UMJm3zBmyvvwGAKiOdpNtuP0gAPxAxHMbx3//iWbQYAHVWFkm334Y6I+OgujqtgSsn3sLnVZ+zpGEJo9JHnRXCWru/nZXVK/m26VuC0SCRcAjREySnw0S+Iw2jKA0OWb21uKztnH/3T1GdJOE84gzS9r9tuOvaiEWjJDcHCJTbMY1JR5V0eooOUVcQ99IGIh1SQlRVSRwOowuVTU/RyDGnuHdHh9Vq5Te/+Q3/+Mc/iMX2Ww5MnjyZ5557juHDh3eWJVx8MYaRI+l48y1C1dXY57yLd80aEr/3vW6/j/soKyvjq6++YsqUKdhsNlasWAGAKb8/+pJ+xDIkL/u5e+bS4m3BoDZQ6ag8SDg/kLaaKrYuXsDEa76HznQaW7ydRFqrKlj98ftEI2HSCksYe/nVKFXS71N6UQnOthZ2rfyajJJ+Z8V186hI+M5v1K5PsXTUMKQOVpkNfLS2jkcuHHDKP7dYTGRDnZ0vtrZQa5NmMQqCQDTgZ+m2JjbVObh+bC6j8g4/o3Fns4ulu9tRpaRwU4qX9DGTUehPz2v20SIIAoZRo9APGYJ76VKmzJ/PKOsqvnJlsTZjANuA7U1OJpemcOmwLBL0p999noyMjMzZjiycy8jInDQi4Sj1O+1kl1lQa5WIokjNVpvk590NlkwD7NVrTmtrlu7QmrpPCBoNSwmEQLJoOcC/MdOs56mrhmA2dJ9sTuYEoFBAyUzY9A7sWQCFU448Gtxrhf/9GA5IqkhCjmTBkjMGzLknosfHRCwm4gtHMWmP/rbgm73R5sNzLRiPcvtAyM8fP/ktCzw7CWo0COpsBmefy5MDhhCn1hAdOBBFfBz6YcMwX3UVCk3P5364tRXbv14lXC/NyjBNnYr58ssQDrHNOVnnsKBmAXWuOqqcVRSZj8I//jRCFEX2OPawJLCEBWsWdIogaYY08qxx6Co7UKNErdWRPWAwmaX9aK+tRq3Todo7mCOKIntWryB7wGAM8Qm93r+26kp2frOMhIo4FKICU1kaAkoirT7sn1aiLjQRPzYHhbZvWhx0R7DOhWd5I7FwFFEhYp6WjyY7jgnkE/R5UarUiDERoReS6Z5IwuEwL774Io8++igOh6OzPD8/n6effporr7yyW2FNnZlJ6kMP4lm2DOd//0eosoqWP/6R+FkXEH/+TIQeBgoHDRrEggULmD59euf+IjWVWMaMZ48/yFSgKKEIg9qAN+yl0lHJlJwp3bYlxmJsWfQlbmsbq//7PpOuvwWF8vQ5h04VTXt2EY2EySguY/SlV6FU7b+2F48eR+W61ThbW2ipKCej5PS34zghRCOw/jWo+AqVSc10bYi14TA7d9ayfXg2g7J69zp6pISjMVZW2pi/rYU2V4BYIIDWaGByaQozypIof/5l3veYsGXk8/elYYZmm7lxXC7Jpu5n3AXCUV5fWQPA1P5pjB0/9iQeTd9B0GiInzkT44QJuD77nEuWL+ei6bP5zGtkQ62dZbvbWV3VwQWD07lwcMYpHziRkZGRkTlyZOFcRkbmpNG428GO5Y007Oxg0rWliCKUjkkjEooSCcUIh6JEgtJyJBTFEK8Bz6nudS/Ssg2Cbsgd17P4WrFIElz1FsSSmTz/VTkDMuKZUpaKRqWQRfNTQeFU2PI+dFSBrRKSiw+u42mH+tWSd/mQq6UyY7IUeaZQS0J57jiIzzy5fT9C/r2imlVVHXxvXC5Tyo48kV4kGmNVlQ2AiUVHZ9PS1F7L/V/8H3sEDzGlBpV+MHcOvYBbFMlo1dJ5royPJ/03v0F5iChRURTxrlyJ4733EUMhFEYjibfcjH7IkMP2IU4Tx8jk4Xy1ez5vLfo7v73ymaM6hr5OOBpmfdt6ltQtod5VT4u/AX2rmuE5o5g98FL6J/bH2drCjsBicgcPI6O4tDOqNDW/sEtbbdWVbF+2iB3Ll5LVbwDFo8Ziyeg5+v9wiKJkydKxvppy3wbszQ0ABE1xZA4bRNaE4agFLd61Ldg31+FavAe9qoXCsaNPG+FT0CoJuNxYXQ10JFqZkjmwc51K1OD+ugEEgbhJx/45nmi++OILHnjgAXbv3t1ZZjQa+eUvf8kDDzyATtdNjo4DEBQK4qZORT9sGPZ33iGwdRuuzz7Dt34diTfdjLawe5udESNGMH/+fGbMmIHb7SZSU0ViYiLl3iAAheZCjGoj7f52KhwVPVqxCAoFYy+/mqVvvEpHYz2bv5rH8PMvOo5P5Oxg2IzZxCenUjBs5EHfN43eQOHIMZSv+oadK5aSXlwqi4DdUfctVEh2RkJqKXnXzWbcu0tZ0a5m7poaBl425KR+bv5QlGXlbSzY0YrDFybmdqFpb2WCvYrLfnIDSaW5RB0OijUhfurezJJAG8vsxWwKZrOrxcVlw7M4r38ayu8M9H28sZHWVjvJKWauHtXzzI+zBaXJhOW6a4mbOQNVYiI/Ana3uHnr45XUtkepTDUiDOmb94IyMjIyMt0jC+cyMjInBVEUqdkiRaZm95OShCkUAiWj0nrcJhwOU/vFppPRvRNPNAxrXpHsWYK3QenMg+uEA7B9X9LQK9jQ5GNrg5PyVjfjipJkL/NThS4ecsdD82bw2YC9wrmrWRLL61dLojpIHugDLtnvhT7zj90nf+1DRKIx1tfaEUWRN7+txReKMntwz1YKB7K5wYk3GCHBoGZA5pEnj/xm5xL+b9VfcaoAQUeyeTzPj76c9C8W07Z6NUm334Zh1CiAQ4rmMZ+Pjrffxr9+AwDafmUk3XorSrP5iPsy1jySj21vsVWwUtO8h/yM3k9kebJxBp0sb1zON43f4PB24He7CLk8ZDSrGKkpYphlAAOSBgBgTs9gwtU3HrZNlVZLSl4B7bXVNOzYSsP2reQk9CcjpRhTWjLqBD3KOA2KOA3KODWCRtmtKCSKIqFaF75N7Tirm/B02IhovSj1agqGj6JkzIQudhpx52azq2YFLncr7uVbqNm5kaHnzcKSkInK3PcSuorhGIJagbOthW1LF+KytxBQ+ND4Dbg7bMQnp0j1/BGCVU4A9AMS+5wVzc6dO3nggQeYP39+l/JbbrmFxx9/nMzMoxN+VBYLyffcg3/9euzvvU+kuYW2v/yZ1AceQFtY2O02Y8eO5fPPP+faa68l5HWRnZVFayiMPRwhy5SFRWeh2llNm68NW8BGsr77wbu4xGRGX3wF3340h5pN6zGnZVAwbORR9f9soKOpAXN6BgqFEkGhOKSVUPHo8VStXyNFnVeWk1EsR50fRP45UP01tO+CUT/AYM5nxoLFrHGFqCivZ0NdLiPzDp00tzdw+sMs2tnK4l1t+IIRok4nRmsz5ziqGBNpR6sS0LY2QmkhSrOZ1IcfxrN0KTM/+ZQh1g4+9rZTn1bIe6EIq6ps3Dohn7wkaRZoRZubhetrCFZWcrEniFbsD5weA5snGlXifuu6YpPAneXz2WLOZ8Too7T8k5GRkZE55cjCuYyMzEnBWu/BYw+gVCvJ7n9sPsinNTs/lURzvUV6mOqO8vmSJ7YxmVjhVD7+bBcAMwakEa+TPRFPKSNuAvVdoFRD5RLY/QU46g6oIEBqP8gZ19V6qI+L5gCV7V5CkRgKhUAsJvLR+ga8wQhXjcw+bDTcvqSg4wuTDopC6w5RFPmwaiEv7Z6DUyWAwsw5eTN4PGMU3r/9C197OwgCEbv9sG0FKyux/fvfRG0doFSScPHFxJ0/86gj+EpyBlIaV0S5u5K5q9/igcsePart+xL17nqW1C1hXes6PE47frcLtTdKaTCDwmARbqeboVPPoWD40YmGoihiNqUy5pzL8U31UrH2Wxp2bEfZLGJvqsVOLWmFxQiCNLgX8LqJ6UE/PQ2j2YI+Lp5QvYdYIIJ/ezsxVwQAvSWB+kA5aSP7UTJhQrf+04IgMPrma6jZspEdXy/GbW1jw1tzKVINxTIiH8vEPBR9IN+DKIoEKxy4VjXQZKyhumIjiCIKjZLikePpN34y6gOis1XJerSFCQSrnHjXtRI/M69PRO12dHTw6KOP8uKLLxKN7s/JMGHCBJ5//nlG7R3QOhb2eQFr+/Wn47XXCGzfjvXll0n7xS9QWboXECdNmkRFRQVffvklaxLiaAhF2OMNMMZsothczE7bTnxhH1WOqh6Fc5B8uQdMmsaOrxexeeE84pNTSMruO3ZZp5rG3TtZ+8lHZPcbwMgLLztsQmCtYX/U+a4VX5NeJEedH4QgwJRHpGTwaj0CkHXJbM557QsWW9V8vKaG4TlmFCfIqqnNHeDL7a2s2GMlFIkQ7bCTaG1ksruaYZEO1FoNpqnTiZs+rctgs6BUEjd9Ovrhw9HOmcNdW7expt7KfGcxNdFifv/ZDmYMSGP24Az+vbyKYH0Do8JWBiYlH9Ia7WxGYTSSeO11nJeUiFZOEiojIyNz2iEL5zIyMieF6r3R5jn9Lag1Z1k0ite6P5J82I1dfMs7CXlh5yfS8uBrWFXrpNkRwKBVcf7A9JPXV5nu0R3gRRpwSqK5oIS0gXs9y0d3rXMasbPZBcCoPAt5SUY+WFfP/G0teIMRbh6f3+NDvSsQZkujFDE7sfjwNi3egIc/bn6T5c1riYkCKUnDubNsFudXduD8y3MQjaJMTCTpB99HW9yNHc5exFgM17x5uD77HEQRVUoyiT/4AdqC7i0fjoRLhlzFMyv+xJq2tTjcNsxxScfc1skmJsbY0r6FpfVLqXBUACAiYuiIMtiZQ04kmcyCEjL7DWLTnkqGzLgA9REkoY16QoSbvYRbvISbvcR8EQStksRryxh10eUMPHc6dfPWYa2pQUBBXnEiUU+ImDuEr8mJo72Nxne/BEBQKCkODkYVU6HSarHkZKEfmISufxJJ9Ed1GLFFUCgoGDaSrLL+7PxmKY5Vdfg9LoLLt+GrsJJ0ThEqiyRKCxol6gOSSIdbvIiRWPftqhSo0/fnzwi3+RBD0W7rolSgOSDXRrjdhxjcXzdY5cS7ux1bfS0elRW0Iln9BjLw3OkYzd2LwoYRqQRrXdLn3OhBkx13yM/hRBKJRPjHP/7Bb37zGzo6OjrLc3JyeOqpp7j22mt7TRhVmowk3XkHbU89TbixEevf/07qgw/2mMNArVajUCgoMWhpCEXY7ZOE80Jz4X6fc2clYzIOnWy1dNxEnG0tNO7azuqP32fqrXeijzvymTJnKg27trPu07mIsRgi0vXjSP7T+6LO1Vod4WAAjU4WBA9CoQTF/s9FP3w4079YwMqOEHUV9ayqzmHCUdqcHY76Dh9fbG1mbU1H5zh+QZKRsbsX0c9RjzIujrhpF2OaPBmFsef8QarERGmWyMaNTHjvfYaZrSwsmcC6GjsLtreyZFc7vqZm4gIuLtF2YL72nl49jjMJQRAwjjs7vd9lZGRkzgRk4VxGRuaE47EHaa91gSCQP6R3HxBOCza+KVm1pPTrOdp81+eSeB6fRSR3Iv/77w4ALhiUjkEjX6oBojHxiKKaTzh5E0FvhqyRoD11QldvsWOvcD4gM55JJSkYtUpeX1nD8j1W/OEYt08qQK08OPpwVaWNWEwkP9lI5mEiqNbXbOL+ZU/j14toNAZG5szmdwVTiLz1Js6d0swK/YgRJN54wyEf5CN2Ox3/fo3gnj0AGMaOxXLdtSj0xyfYjB0wlbS1r9IasvLu1/9iyvALiWoFPCEProCLPTvW4Qo48YQ8eMIevBEvvqifHEM2v7vqL+i03QyGnWB8YR+rmlexsPJLmqy1BL1eUnLyGZE2gik5UxCSOggF/OQMHII+Lp5wOMzmqprDthvY3YF/u42oK9R1hVJAlahDDEURdCr0cfGUXTONMiASCnURv1uXNRFtUmLyJOFzOohFongidlSimkDUT86FY9EmSN8dxVFM69foDQydMRvn0Ba2f74ITS0kKjX41rcB4LZZ8ePBnrN/xkJibSLKsGQbc6Anu6fDhj/moSN3v0hsqbegCkrXW0tGVqdQ7LV34A97sOXbOuuaG8yoA9IAhCU9E0GhQKXR4E8LEY1XcO7020jMzD7k8ShNGvT9E/Fvs+Fd14o603RKEoUuXLiQ+++/n+3bt3eW6fV6HnnkER588EEMht4/vxVaLcn33E3rE08SrqvH/sYbJN522yHF+VKDliUOL+XeACAlCDWqjDQFm6iwVxx2n4IgMOKCS3DbrFjSM9Ac53WjrxKNhHG0tBDweggHA4QDfizpmSTn5gPgsXew7tOPCAUChAMBQn4fALmDhjLigksOG22+D63BwPTb7+n1ZMFnMoIgkH7pRUx5ZS4Lw0n8b1MTY/ITUXXzG3s0iKJIeauHL7Y2s63RSSwcJmq3M2xoEbOHZlKWFocvxY0YCmEcP/6II8MFQcAwYgS6fv2IBQLcnZjIlgYHb66oprW+mUh7O5cFa8n8wdWHtFWTkZGRkZE5nZHVGBkZmRNOzVYp2jw1Lw5jQt/zpD2htGyFulWAAKN+0H1S0IATdn0mLQ+5luUVHVg9QRL0aqb1O/JEjWcyq6psvPFtDYOzzHx/Yj469SmctWBKAdOUU7f/XsQXilDV7gVgQIYUeTmpJAW9WskrX1exrqYDfyjCPVOLD/rMV1ZKIuI5h4k2f27567xZ/RlRRRQhpOfO4Xdye/FoAtt3YN25C0GtxnzttRgnTjikaObbsBH7W28R8/kQtFos11/faxFcCoWC80sv5I1tr7OwYRHL7auxZEgeziIirY1S8sHvssdTxV8+f5RfXP7kSbMpaPe1s6jmK5buWYjTaSPk96MVVZQF07l25I0MGDROqjjs2CLwo96wJJoLoErRo043os4woU7RI/SQZ+G7EeP9z93v4SqKIn63C5/DTsDnJTk7D+1xCiwJqemMv/UG7M2NGLxGAnscEInht3lwetpoq95vo6T0R1GLahAEUhKLOsv9HV6cnnbaqms6yxSBXDQx6TcqJbGo838acPpw+tppq67urCsEstHGJOE1yZKPyqDFMCyFIfEFqHX6Iz4f9ENSCOxxEHUECVY40JUem+fxkiVLWLduHZmZmeTn55OXl0dGRgbKQyRS3bNnDz/72c/49NNPu5TfeOONPPnkk2RnH1r4P15USUkk3XkH7c+/gG/detRZWcRfcEGP9UsM0v+m1h/CH42RH5/PbYNv4+2db9Pia8ET8mDSHPrcUmk0TL7hVlRa7RllLRLweKjetA5rXQ0dTQ3Eol1nTpSOO6dTOBcEAXtzU5f1BcNGMXTGBUcsmu9DFs2PHt2ggVz+QBwbVndgdQf5psJ6VEm5D0QURTbVO/hiazNV7V5iwSDR9nYG2KqYGmxi8LQ0DOnSb7tx/Phj7rPCYECxdwBtSLaZn1HBgtqd6MUIo4YUYBgp5wyQkZGRkTlzkYVzGRmZE040Ejs7o81jMVj3mrRcej5Y8rqvt/2/EAlCYiGRzFF8NncbABcNzTi1AnEfYW1NB/9aXoUowrqaDhrsPn4yrYT0hL7vH97X2d3iRhRFUuN1JJn2D2qNyk9Er1Hyt8UVbG9y8ZeF5dw3vQSjVrptqLP5qO/woVQIjC7oOWfBDz/5I6uc60EArWDht+PuYnaxZKegHzQQ89VXoRswAHVGz8lIY6EQjg8+wLv8GwA0eXkk3vYD1Km9O6h0/ugrWFQxn5ZgO0ZBT3ZcNka1EZPahDuQgVFlJEFvJsFgwWxMxOZp5/Vt/6FO38HC2oXMzO8m4W8vIYoi5fZyviz/grXVK/B73IgxkYSYnuHBYsZkjKV48EgySo88QZ8oioSbvAR22tD1T0KTJQmOurJElHEatPnxCL1w/REEAUN8Qq8LbIIgdEZ060oksTlUI2Dy5JDFwSKOIIB54H7hPFynxOjKIpPh3bZvHlDYKSJGG9ToHRlkMKzbupb+xSgOIVAfCoVGiWFoCt41Lfh32tCWmI9K0K2qquK+++7js88+O2idWq0mNzeX/Pz8zldeXh55eXl88sknvPDCC4TD4c76Y8aM4fnnn2fcuHHHdCzHgq60FMt112F/+22c//sEdUYG+mHDuq2bqFaRrFFhDUWo8AUYHGfg3JxzWd64nBZvC1XOKoakDDnsPg/0mo9Fo1RuWEPRiDHH/D882UQjYTqaGlEolCRl5wAQi0XZtWJZZx2dKQ5DghmNTo9apyMhJe2AdSbGXXnd3nV6tAYDWkPPM32OhKDPS/32rRSNGntGDUicCARBIK4gjwv9Ot5dU8dnW5qZUJR8VAngI9EYq6s7mLetmWZHgJjPB9Z2htsqmRRqJlkMoi0pOapE2UeDISGeabSjMBqwXH/dCdmHjIyMjIxMX0EWzmVkZE44Q6flUDomDZ3x1CdwO6koFDDq+7DtIxh8dfd1vDbYs0BaHnItKpWSn55XwqKdrUwuSTl5fe2jbKyz88rXkmg+Is9CVbuXFmeA33+2g9snFTA899iiM2Ukdja7Acmm5bsMzEzgZzPLeO6rcirbPDw1fxcPzCgjwaBmZaU0i2RYrhmTtvtbiSprPaudGwHIUufx77H3IPz3cyI3F6FKkjzE46ZPP2T/Qg2N2F79F5HmFqn+zJkkXHIxgqr3b190Gj0v3PImsVgMxXejLgd1v40+PYkPyj/k08pPSTemH5FodzSEo2HWNq9haeMymjxNREJBfC4XmREzI7QDGD94OrmDhhyVKC1GYgSqOvDv7CDqCEplIp3CudKoRllyen6vUvMLj7huyt7o2yMhKTv3hCaS1JUlIkZF9P0sRyw6BgIBnnrqKZ544gkCgUC3dcLhMJWVlVRWVh6yrczMTJ588kluvPHGg8/9k4Bp0jmEm5rwLFmC7bX/kPrQQ2iys7qtW2bQYQ15KPdKwjlAYUIhLd4WKh2VR/0d3Dj/M+q2bcJaW8OYy65Cqep79yn7hHJrfa0UUd7YQCwaIa2wmAlX3whIkd9Fo8YSn5RCcm4+Rktij+eSUqUmo/jIB9mOpH9f/eslQn4fcUnJpBX2nKNCZj/nlqYwf2Md7Q2tLN2dxswjyGcTCEf5urydBTtasXtDiLEYitpqxndUck6klTgxjH7oEOLOPx9t4ZFfD4+W+FnnYzznHASBQ9qrycjIyMjInAnIwrmMjMxJQW86Mj/FM470wdKrJ7bPhVgEUvtDxlAAchIN3Drx2BMdnilsbXDy96WVxGIi4wqTuO2cAtzBCH9fWsmeVjd/W1zBxUMzuXRYphzhdozsaJaSew7I6N6rvTjVxM9n9ePZheU02P08OX8n900v5dsqyaZl4iGSmj2/eg4iMbQYeSfzCtzP/g0xFMLx/vsk3333IfsliiKeJUtxfjwXMRxBmRBP4q23ouvf/xiP9Mg5GuHw3JwptHhbWd64nH+sfYmfDLibfvlDj7sPNk87n657n6/rlhFSRjGnZ6BRaphUMIkco56youEkZmYf1Xkf84ZJbNPgmlsJYcl2RlAr0Bab0fc/fZKhnokISgHD4COfkTV//nx+8pOfUFGx39c7MzOTX/ziF4RCIWpra6mpqaGmpobq6mrcbne37eh0Oh588EF+/vOfYzrF/sTmq64k3NxEcNdurH9/ibRHHkEZd/B1qdSoY4XDw+69PufesBdn0EmTp4kqZ9VR7ze7/0Aad22npbKcbz+cw9grrkWt6RuWcqIo8u2H79BeW0MsGumyTmc0oTN1HfAcMn3WyexeJ0qVmtxBQ6lY+y07VywjtaBI/k0+ApQeFxPXfsGHyhw+W2NicmlKj7MM3YEwi3e1sWhnG55gBAGI16uZMSCNAdblKK3NGMeNJm7mzEPO4OrV/ptkwVxGRkZG5uxAFs5lZGROGC6rH4VSgcnSNx5CTyohL2gO81DhboHKxdLykOsIRGKyNctedjS5eHFJBdGYyMh8Cz84pwCFQiBBr+bBmaW8v66BRTtb+XRzEzU2L3dOLpSTqB4ldm+IZkcAQYB+6QdHnO8jJ9HAIxf0488LymlzBXn00+2EIjHi9WoGZfUc6byuQ0r6OdSVgOuddwDQlpVhuf76Q/Yr6nbT8cYbBLZKlkW6wYNIvPnmbkW0vsCVpVdS2biDDQ2reLr5MZ666iWSEtIOv2E32F3tvPLVX9hg20QkJnkUm9BxUf6FTMqdjFFthH7H1k/vskYsHRpiqiiqOC36/kloS8woNPI1py8hiiIRWwB18sGJK+vr6/npT3/K3LlzO8uUSiX33Xcfv/vd74jr5jsiiiIOh6NTSN/3MhqN3HXXXeTl9WAhdpIRlEqSbr+Dtj/9iUh7O7ZX/knKffceNLukzCjZrFT6g0RiIqIosrFtI7aAjSpnFeFoGLXyyKPG0wqLmXDNjXz74Rzaa6tZ8d5bTLj6BjS6k5c4NBwK0tFQj7W+Fr/bxaiLLgckS49YNEosGkFniiM5J4/k3HySc/MwWZL6lDhdMmYC1RvXYW9qoK2mirSCosNvdJajNJuZUJbGkvIA9vpmvtrZykVDMrvUsXmCLNjRytfl7QRDEaIOB+aOFi69eirnDMxGo1IQslyPwqBHldizbZqMjIyMjIzMsSOrDDIyMieMHd80YWv0MGRqDjkDzqIb+radsPQJyZ6l/8U919v6AYgxyBiGK76I//twC6PzLVw7Oget6uwVs8pb3fx18R7C0RjDcszcOakQpWK/QKBSKrhhbC75yQbeWFnL1gYnv/9sBz+aWky2xXAKe356sbPZBUBekrHTu7wnUuN1/GK2JJ43OfwAjC9M6vJ/OZDNtnqCGj/KAFy3KwAKLQmXXELczBmHTD4X2LWLjtf+Q9TpBJUS8xVXYpo6pU8JRN9FpVBx9+gf83DtDjrCTv74v0d44vq/o1UfnQf/2h3LeGnl87iiHgAyVSlMLziPaSMuId58dBHhYlQkVOtCkxOHoJY+b02pGX9tNcZzszDkWxB6+N/JnDhEUcRu/xavr5KM9MtRqbpGeYuRGM4va4hY/ZgvLUJlls6hUCjEs88+y2OPPYbP5+usP2nSJF588UUGD+55VpMgCFgsFiwWC8OHd+/n3ldQmowk33M3rX96iuCePdjfew/LDTd0qZOpVWNSKvBEY9QEghQbTOTE5VDlrMIdclPjqqHEUnJU+03OyeOc625i5QfvYG9q4Js5bzDx2u8dt+93T4QDAWwNdbTX12Crr8XR2oIYi3WuHzxtZue+B02diUqtPqT1Sl9AZzKRP2wkletWsWvFMlLzC/t0f/sKSZdczIwn/s67Th1frKlialkqRq2KJoefedtaWFVlIxqOEOnoIM1azxRPLYOidixNqWiGSvZRPdkaycjIyMjIyPQOsnAuIyNzQnBZ/dgaPSAIJOee2ingJ5VYFNb9W0r26WruuZ69FmpWSMtDr2Xe1mZ8wQjVVi8a5cn3mO0rVLZ7eO6rckKRGIOyEvjhlCJUPXweE4qSyTLreXFJBW2uII9/sZPvTyxgdP5ZNEhzHOzYK5wPyOg52vxAzAYNP7+gH39dtIe6Dh/nlvXswf9h7TdoVBqyAkZKRB2pD/4MbWHP9kNiJILz089wL1gAoogqPY2k229Hk519dAd1irDEp/DIzMf4zbyHqPU38uwnv+Phyx8/ItuXaCzKnG9f5b/bP0QUIVFt5q7xP2Fkv3OOWniKBSMEdtsJ7Oog5otgHJeBvp/0fdAUJdBU7mdYTtxpIZrHYmEUir7nN32sRKN+GhrexOFYC4AgKMnO6ioKCyoFCoMaRD++da3En5fHkiVL+NGPfsTOnTs766WmpvL0009z0003nXHipDojg6TbfoD1pb/jXf4N6qwsdBMndq4XBIFSo44NLh/l3gDFBh1F5iLWtqzFG/ZS6aw8auEcwJKRxaTrb+Gb997E2dbCyg/eYcrNt/fK5xvy+1BrdZ2DhlsXf0nt1k1d6hgSLHsjyvNQHBBlb047vO91X6Fk7ARqNq2no7Ge9trqo8o50JuIotjl//bdv/sS6qwsJgwrZOl2P20NzcxZU4c/FGVTvQMxEiZitZFnrWWKv56SqAuVxULcjGswTpxwqrsuIyMjIyNz1iAL5zIyMieE6i1S8sCMooSzy9+84itw1Ek2LUOv67nelvcBEXLG0qHNZvGuLQBcMfzofIvPJGptXp5dWE4wHKNfRhw/mlqM+jCDCHlJRn590QD+sayKnc0uXl5aSc0gL1eMyO4xGlpGEhI6hfNuEoP2hEmr4pEL+hGKxnqcFRGJRVnVvBoxHObc9jj0g4ccUjQPt7XR8e/XCNXUAGCcdA7mq69GoTm9rhtF2f354Zif8PzqP7PWuoE5S1/hxmk/POQ2Nr+N17e/ToWvAqVKzfD4Qfzkwl9h1B+dLU3EESCwo4NgpQMxKvmXK/QqDryUnC7XFb+/jpbWT3G5NhMfN4TMzGvQalNPdbeOC5+vmtq6fxIKtoMggCjSYfua1JRZaDRdB/qMI1MJ1bsJNbh59Cf/x+/+9njnOoVCwd13380f/vAHzGbzST6Kk4d+8GASLrsM58cf43j/AyzJXQfpyvYK57u9AWanQJG5CIPagDPopMpx9D7n+4hPSWXyDbfy7UdzGHju9GP+zgR9Xqz1ddjqa2mvq8FlbWPKzbdjSZdsOJJy8rA21JGck98plh9Ngt++it4UR/7QEVSuX82uFctIySs4IdedSCiEvbkRv9uN3+3C73YRcLvwuZ0E3G6KR4+jdNw5gBTdv+zt18gfOoL8IcNR9cHflYRLLmLm5ud5w61nxdY6FAYjxGKU7FzLZH89uTEvqox04s+/HMOoUSckObaMjIyMjIxMz8i/vDIyMr1OyB+haY8DgPwhR57w7LQn4ITN70rLQ64DXQ+CZMs2aFwHCDDkGj7b0kQkKlKSFsegrCMXMc8k6jt8/HlBOf5QlOI0Ez+ZVoJGdWSR93E6NffPKOWjDQ18ua2F+dtaqOvwcde5RZgOY0FyttLsDOD0hVErFRSlHN2MEEEQDmkl9PLaj7B7WjHq4rn23GuIy+x5Grl31Wrs776LGAigMOixfO8mDCNOrZ2EKIqI4RhiIEosGJHeAxGUcRrU6ZJ9QswfwbWoDlWKHuOY9E5xaNLQ86mzVTN3z1w+rphLblIBk4ae3+1+Fm35nPn2pfgjfgwaAzef90vG5U7stm6PfY2JuBbVEW70dJapEnXoBiahzY9HOI1mr3QK5s5NnWUu12bc7m2kpJxPaupslMrTK1+GKIpYrV/R3PwRohhFrUkiL/cOmlvm4vWU09Y2j+zsG7tso4zXoiuzULNkB+zc/38dM2YMf//73xkxYsTJPoxTQtzMGYSbmvCtXo391VdRHnBdKN3rc17uDSCKIkXmIoxqIy3eFiodlcTEGArh2M59U2IS02+7G4Vi/zVOjMUOaTEF4Gpvo2rjOqx1Nbht7Qetd7a1dgrnuYOGkjd42DH1r69TMm4idds2k5CaRiwaRXkUIm8kFMLvcRFwu/G5nAQ8bvwuSRjPLO1P3pBhAHgddr55940e2/G5XJ3LNVs24La2sXXRfHav/JqikWMoHDEajb7v2LqpU1MZPbo/326yUWttZ+K0PC4YnI5WtZtwS4j4WeejGzz4tBn8lJGRkZGROdOQFQUZGZlep3a7jVgkRkKKAUt633k46TViUQh5QHdAhFjtt1C5CMI+sORD8XndbxuNwPrXpOWS82gTklm+R0qCeMWIrLPywajJ4efPC3bjDUYoTDHy0+mlR50kVakQuGZUDgXJRv79TTU7mlz8ZUE5D88qkxOudsOOJklYKEkzHfEAxZHyScUyIrEgZoykTJrcbZ1YIIB9zrv4Vq8GQFtSTOL3v39Ckpvt3r2bcDjMoEGDDlmvU4Ru9kJMPGi9rsTcKZwLKoGI1U/E6kdQKzCO2J8M9Popd1Jvr2GtdQOv73qDkuKhpBv32y14/W7++vkfWGvbSHxKCoNyR3DLwFtI1h/ZIKMYFRGU0nVCUAiS7YoAmtx49AMSUaUaTqvriN9fR0vLJ7hcm6UCQcCcMAqLZTzt1oV43Dtpa/sCu/1bMjKuwmwefVocXyTiob7+NVwuaTZRQsIIsrNvRqUykp52CZWeZ+joWE5q6gUHRZ2r+iew8YVN5JozGJ87nCvvvZH777//iKx/zhQEQSDxxhuItLYSqK4icckSxGuvBSBfp0WjEPBEYzQFw2TqkkgzpFHlrKIj0EGzt5ks07H7Ph8omrus7az57weMuuhyzOkZAPhcTmz1tcQlpXSWBf0+qjeu7dwuPiWV5Jw8knLySM7JQ2fcP0B5Opy/x4reFMese+4/KLI7Egp1Rofve1nSM0krLAbAZW1j0at/77nd+IRO4VwfH48pMQl9XAL6uHj08fHSuykOfXw8hnhz53aFI0aj0mjZs2YlXnsHO79Zyp7VK8kfPpLi0ePRm/pG0umEi2bzg7W/JenGizEW5iMIAuJ114FafUafLzIyMjIyMqcDsnAuIyPTq8SiMeq22QDIH5p8+tzwhwOSGG48QLyq/hpslVIkedAFfof0HvSAQgnXvkWnF0LdSmjZKi2P+gH0JHDs+RKcDaAxwZBr+d/qJmIxkUFZCZSm9Y0HuJNJqyvAMwt24w5EyE0ycP+MUvSaYxe6R+cnkh6v488LdlNr8/Likgrum17So0/62crR+psfKVXWetpjrQBcWzq92zqhmhpsr/6bSLtkWxF/0YXEz5qFoOzdAY5oNMpjjz3GY489hkKh4Msvv+S883oY0AIiHQEiLftFc0ElIOhUKLRKBJ0KpeWAZJ8qBboyC4HddvxbrKjMWrSFZkCy03jgkkd5YtGjtKmdvLz5ZR4a/RBGtZHtVet5fumfsIUdCAKMNwzjthE/Rak4/LFHPSECOzsIVDgwX1yIcq8FlnFUGsax6Z1/n2xisTCCoDrqa73PV0vrXksWQBLMzaNJS70QnU6KzI2LG4TLtYnGpvcIh2zU1f0TW8cysjKvQ6/P6e1D6TU83j3U1f6TcNiOICjJzLyWpKT9SW5NpjKMpjK8nt20tX1Bdvb3umz/yn/+xZfrP+WqQbP4yXm3cu29PzqrRPN9CBoNyT+8i8Zf/Qp1WxuR1lY0OTmoFAJFei07vQHKvQGydBqKzEVstW4lFA1R6ag8LuH8QHYsW4Tb1s7yd98gs6QMW0MdXocdgKKRYzuF88TMLIpGjSM5J5ek7Dy0hjMwaOAI2Sea+5wOvv1oDn6Xi3AwcFC9guGjO4VzvUn6LVKpNejjE9DHxUlieFw8urj4Ll7vGp2eGXf8+Ij6olSpKRg2krwhw2jatZPy1StwtrVQseZbajauZ9Y996PWHV0i5xOBKjGRlFtvBYcdQZC84YU+aCsjIyMjIyNzNiIL5zIyMr2Kxx4kGomhMajIKD6Fnp2iKAnhQTfEZ+4vr1wM1gpJDA849ovikSAISrju7f1ieMNaqF/TffuxqBRdrpEiUMkcDoZkSOkHKWXdb+Pr2OttDgy7AS96Njc4ACna/GxBFEXqO/xsaXSwZFc7Tl+YbIuen80sw6A5/p+lnEQD951XytNf7mJHk4vXVtRw+6QT47V6OhKJxtjd4gaOzt/8SHh21duIYgxjSMcFVk2XpGyiKOL+cgHOTz+FaBRlYiJJP/g+2uLiXu0DQEdHBzfeeCPz588HIBaL8ctf/pLp03v2LVYn67FcWUK4xYcmNw7hEJH4giBgGp+JoFHi32rFvaIJRZwGdYoklmnUWh6Y9gueXvs0Vr+Vf235F4l2JZ9WfUZUjBGvNHHPhPsYPeDcQx6HKIpE2nz4d3QQqnPB3kD4YKUTw1DJ91mZcHLtS6LRAF7vHjyeXXg8u/AH6hEENRpNMlpNMhpNMhpNChptChq19PeBFivdC+Zj9grmGV32JQgCCQnDiYsbSFv7l7S1zcPrKad8z+9JSjyX9PRLUan6TvJpUYzR1jaPltZPQIyh1aaRl3cnen3uQXXT0y6m0rObjo5vSE2dhUYjDdra7XYeffRR3A4X0wrHceFF5x7yXDzTUZrNaPLzobGRUEUFhhxpwKTUqGOnN8Bub4CpSfFcVXoVaYY05tfMp9JRyeTs7me7HC0jL7qMVR+9i7W+lrpt+89ZS3oGBrNlfz9VaoZM796W6WxFrdXham/r/Ful1XVGhevj4knOzj1gnZaL7vv5CROxFQol2QMGkdV/IK1VFZSv+gaj2dJlfxGfl1gsCpyapMSGkSNPyX5lZGRkZGRkDo0snMvIyPQq8cl6pt8yAI8jgPJkRvlWLYOabyQRPOCEgAvEKAgKuO6d/WJ40yaoX919G4LQVQzPHg1xGZIli84seZbrEqSXJq5rVHnRtMP3cdM7EAlAUjEUTcMoCDx55RC2NDjISzIez9H3eXyhCNubXGxtcLKtyYnTF+5cl56g44GZZb3qR16QbOSeKcU8v2gPq6psJOjVXDO670aonkxqbF4C4ShGrYrcxN6Nitxg34UgwtB2DaH29QgzZgAQdTiw/ec/BHftBkA/cgSJN9yAwtj75/2GDRu48sorqdmbbHQfa9euZcGCBZx/fs/ilsKgRlt45AN+huGpRB1BQvVu3IvrSbioEKVREl3iNHHcNfQunlnzNCs2zScaiQIwMKEf98/+NZb4lB7bFaMiwWongR02Ih37IzXVmUb0A5JQZ508sTgWC+H1VeFx78Tj3YXPVwNirGt/xRDBQBPBQFO3bahU8Wg0SQgKNV5PuVQoCFjMY0lNuxCdNr3b7fahUGhIT7uYRMsEmpo/xOlYh822FIdzLRnpl5OYOAnhGD2te4tw2Eld3b/weHYBYLGMIyvrez36sptMZZhMZXg8u2ltm0dO9k0A/PGPf6SjowOA2lQXJVeM6BzsiQUjxDxhVEn6k3BEfQdNUTGsWEGosgqmTgWkBKEA5T7p+5GgTaDEUsL8mvmU28sJRALoVMcvwqq1OsZffSO7vlmKoFCQnJNHYlY2au2pj1Lu66i0WiZc8729keNxh/zMBEE4KZHfgiCQXlRCelEJ0cj++xC3zUrbmm/4or6S+OQU4pNTiEtKJi45lbikZEyWRBS9PCtKRkZGRkZG5vRAFs5lZGR6HaVaQULKSZymvP2/sHlO9+vU+q5ieO54MOceLIRr46W6B0ajFvROxBoAbTuhZjkgSFYu+6bsa1VMKDrzEqjuiyrf2uhka6OTijYPorjfN1qjUtA/I57BWQmMLUzslUjz7zIoK4HvT8zn1eXVfLm9hXi9mlmDDi3QnQ3saJaizftnxPdqFP5nO5fhxYVChOua1BivGg+Af+tWOl5/g5jHg6DRYL72GowTJpyQGQCvvfYa99xzD4GAJKYlJydzyy238Oc//xmAxx57jJkzZ3bZtxgTiXYEUCUfvRgpKATiJmfh+KKaqD2I5+sG4mfld7afZcri8uRZvFCxA7Wg5Kp+13DFObcc3nYjFsO7phkxFENQCmiLzej6J6Iyn3hhSRRFfL5q3J7teD278foqEWORLnU0mmRMpn6YTP0xGosRxQihUDvBUDuhkJVQ0Epo73I06iMScRGJ7E3YdxSC+XfRaJLIz7sLd9K5NDW+SyDQSEPDW1htS9DpTu2sHY97J5GIG0GhITvrBhITD5/oNS3tEjyep7F3rCAt9QIaGlz89a9/BUCn0/HgY4+gjD8gWn9jG4HddnRlFgzD01Bozw4hT1MkWVeEKis7y4oMOgSgPRShIxwhUa2iMKGQBG0CjqCDN3a8wR2D7+iV64xKrWbQ1BnH3c7pjj/iZ2PrRoamDsWoPvygpyAIpBUUnYSeHRtK1f7IcltDHYJSSSwWxdXeiqu9tUvdwdNnUTxqLAB+twu3zUpqfuFJ7a+MjIyMjIzMqUEWzmVkZHoNl81PXKLu5FtiJGRLNitlsyB98P4IcW08KL9zmcsbf3L7BpKty7p/S8tF0xATC6lu91CQbDyj7EN8oQg7mlydYvmBUeUgRZYPyU7o9HNXn4QZCROKknH5w3ywroEP1tWToFczvijphO+3L7MvMWj/jN711H992xcgQqrXSIGoxTBqFK5583D+7xMA1Dk5JN32A9TpvT94EQwGuffee3nllVc6y8aMGcOHH35IVlYW8+bNY8eOHaxcuZIlS5Ywbdr+GSKhWhfuZQ1oCxKIOzf7qPctqJXET8/FvaQe45j0g77Tk4fMIt2ShUkXT2ZKXo/txIIRFHtnXQhqJfpB0oCarszSWX4iicVC2B1rsFq/IuBv7LJOpU4gztR/r1he1mkrciBabSrdnVGRiFcS08NWImEXprj+Ry2Yf5c4Uz9KS3+N1baUlpb/EfA3HtTnU4FOn0Ve7l0HWc70hMlUisnUD49nF61tX/CLX3xOKBQC4P777yc3d7+VhSiKiOEYiBDYZSdU48IwKg1tkfmM+h3pDnVBASAQ7bARsdtRWSwYlApy9Rpq/SHKvQHGmU182/wt0VgUZ9DJlvYtfFH9BRcWXniqu39G4A17+dvGv1HvrmdHxw5uH3z7qe5Sr5I/dATpDc1MPWcifocdt82Ky9qO29aO29pOfPL+GULWuhrWffYxQ867gKKRY05hr2VkZGRkZGROBrJwLiMj0ysEvGG+eX8PxgQtE64sRt1bkXAHRCn3SPYouPCZrl7mfYk9C8BRJyUEHXodm+od/G1xBQMz47l/RulpK3qIokiDXYoq39Jw6KjywdkJJJtOrhfzPs4fmI7DF2bhjlb+vaKaOJ2KQVmn0H//FBIIR6ls9wC9628eiUWpjTQjiCITWtXohw5FFMH5+ecAmKZNxXz55Qjq3veOraur46qrrmLt2rWdZXfffTfPPvssWq10zv3qV7/ihhtuAKSo833CuSiK+Da3A6A0H3siNqVJQ8JFhT1+l0tzBve4rSiKBCsceNe0EDclB81eGxbDkJ6tXHqTcNiB1boEW8fXRCPSuSEoNMTHDcYU1w+TqR9aTdoxX6dUKiMqlREDPQ8aHAuCoCQleToW8xiczg3EYqFebf9oUSoNmM2jUSiO7jxKS78ET8UuKis+Y8HCuQCkpqbyyCOPdKknCAJxk7LRFlvwrmoi6gzh+aaJwB4HprEZqBIPPxthn/iuOI4EzKcChU5HOCkRgFBFBarRowHJruVA4dwb9uIJeygxl9Dub2de9TyyTFkMSx12Cnt/+uMJefjrxr/S6JEGpza1baLB3UB23NEPNPZlBEHAkGAmITmF9OLSznJRFLvciwZ9PgC2LVlIck4uCanyTDYZGRkZGZkzGVk4l5GROYhIOErtVhu2Rg9Gs5aEVAPmVANGs6ZH8aR2mw0xJqLWKg8tmntt0LQBQl4I+yHshXDggGX/3pcPZdDLUJsX7AMhtWR/G6II2z6SrFRMqVJZXxXN/Y79CUGHXkdIZWLOmq0A5CadfhHnRxJVvk8oL0mNQ9MHktoJgsC1o3Nw+sOsqe7gpaUVPHx+P/KTz2xf+e4ob3UTi4kkm7SkxvWe7cdnDZtQ65RoQzqubVNjvGYCvrVrIBJFnZOD5Zprem1fB/LVV19x3XXXYbPZAMne4uWXX+aWW27pUu+aa67hd7/7HeXl5Sxbtozly5czadIkQrUuoo4ggkaBrv/xzUQ48LscbvcR80XQ5h16cCIWiuJZ2USoRpoFEKxwdArnJxqfr5r29oU4nOs7PcvVmiSSk6eSlDgJpfIk2m0dBypVHElJh06y2pcxGUswmfrzzYp/MmWKkf9+7OLRRx8lPr77c0eTYUR9STH+HVb8m9uJtPpwfFpJ3LnZaPOlAcFwm4+oO0TMGybmDhH1hol5wkQ9YVSJOswX7beYODCBb18mmJYGVivBigoMe4XzUoOOBbjY7ZWsmQoTpOPyR/1MzJrIisYVvLHjDVIMKWSZzp4E3L2JK+Tirxv+SrO3mThNHBnGDMrt5cyvmX/GRZ33hCAIXWz8ikaNpb22mpbKctZ+8hFTbr4DlebYB15lZGRkZGRk+jaycC4jI9MFW5OHDV/WEvJJnrbtde7OdSqNkiFTs8koNgMQi4kIAsSiInXbJeEqf+gh/LpFEb5+Cuw1R9aZWAxd2Ily+dMw6wkwJkltrP0XVHwF1ctg9p9B1YcfWDbPkTzWLQVQNJ15W5qxeUJYjBouGnJk0/lPJQdGle/zKo/F9kdeqZVSVPk+C5aUuFMTVX44BEHgB+cU4A5E2Nns4rmvyvnl7P6kxp9dCd722bT0ZrQ5wKe13yBGIgxymYlLSEA3oD+tT/4PAOP4cb26L4BYLMaf/vQnfvWrXxGLSaJvQUEBc+fOZdiwYQfVVyqV/N///V+noP773/+eL7/8sjPaXN8/qdeicCNWP875NQiA4oIC1D14p4fbfLi/biDmCYMgJRrdZ89y4ojicK7FYV+Kz1fdWWo0lZKcPI2E+GEIwukVjXwmsGmzFrvdzvDhepoaM7n99kMLkoJSwDA4BW1BAt61rYRbvKjT9w8Eer5pJOrqPgI/dsBgZywUxTmvGv2AJLTFfdvyJZS6Tzjf73NeujdBaH0ghDcapTChELPWjCPowB6wU2oppdxezitbXuHh0Q8fkS+3zH6cQScvbHiBVl8rCdoE7hh8B1usW9jZsfOMjTo/EgRBYMTsS1n82su4bVa2LPqSERdcfKq7JSMjIyMjI3OCkIVzGRmZLpgsOqLhGPp4DfmDkvF7Qjjb/DitfiKhKFrjfquFxnI7O1c0Y4jTEPJH0JnUpBcewgKjaYMkmqu0kDMONAZQG6SknGo9qI2g1u0tMxCNivjn/BT8dlj2JEz/LWx8C6qWAAIMvKJvi+bt5VC1VFoefRttnhBfbG0G4NrROejUfVOg8oUkcXlLg5NtjS4cvq4CTFqCjiFZ+73K+0JU+ZGgVir40dRi/jR/F/UdPp79qpxHLuhPgr737UP6Kjube184r+poYGf7JhAUXGbsh/GcQsJNzYTr6kGlxDCmdz1gnU4nt9xyC//73/86y2bPns1bb72FxWLpcbsbbriBRx99lKqqKhYuXMjGed+Sa09AUCvQDUjstf4pE3VoMoyEGjy4F9eRcGEhygOum2JMxL+1Hd+mdhBBGafBNDkL9QlMqByJeGhvX4JW9z4NDUYUggJBUGK2jCUleTp6fe7hG+mj+Hw+Vq5c2ZkQ9lRhsVgYN24cSuXRXdeDwSAPP/QXJk0OUVys4dFHL0alOrLbc6VJQ/zUHGK+MArd/m1UKXoURjUKoxpl3N53kwaFSY3CsP9cDOzskJLarmgiWOnEND4DZULfHPwMpaXCju2Em5qIeb0ojEYsahVpGjWtoTAVviBD4wzcPvh2ntvwHDtsO5iSPYWOQAdWv5VXt77Kj4b9CKWib/7u9jXsATsvbHyBdl87Zq2Z2wffzru736XJ00SKPoWOQMdZFXX+XbQGA6Muupxv3nuT2i0bSC0oJLvfwFPdLRkZGRkZGZkTgCycy8icxYgxkaYKB7ZGL0OmSlFDWr2KcZcWEZ+sQ3FA8sZYTMTTEcBo2f9Q7WzzEw5EcAak6PS8QckoFIeIWNv+X+m9eAaMuOnwHQyH2ZV+OXnC12Cvhfe+B0q1lAh0/I+gYNJRH/NJIxbbnxC0cAoklzDnqz1EoiL9M+IZldezwHeyOdKo8sHZ8QzKSuhVi4+TjV6j5P7zSnn8i520uYI891U5P5/Vr88OYvQmTl+YBrsfgH7pvZcY9M8r38TndWJSJ3L+D+4DwP7BBwDoBw9Baeo965Ft27Zx+eWXU1FRAUiRf7/73e/41a9+hUJx6AEclUrFL3/5y85o3q1zV5N7zkx0/RN7NfmmoBAwTc7G+UU1UUcQ95J6EmblI+wdYAo3efBtlCLdtUUJGMdmnDDPab+/Eav1K+yO1USjQQTBi0qVQUrKNJISz0Wt7t2ZB90hiiI2m42qqiqsViujRo0iNTW1V9p97733ePDBB2lsPPWJQQHGjRvHnDlzyM/PP+JtXnzxRaqqqgiH1YwbV0pGpoNgsA2t9sg/owPFcIC4SUcWBawfnIygFPBtbCPc4sX+SSWGISnoB0nlfYmYXo8qNY1YezvBqir0g6W8AaVGLa2hMOXeAEPjDOQn5HNDvxt4Y8cbLG1YyqVFlzKvZh7l9nLmVszl6tKrT/GR9H1sfht/3fhXrH4ribpEbht0G2/vepsmTxNJ+iR+MOgHPLP2mbM66hwgJa+AsnHnsPvb5Wya/xmpeQVo9KeHxZWMjIyMjIzMkSML5zIyZyGiKNJa7WL36hY8HVKUXlapmaS93rrmtINv/BUKgfjvWA70n5BBdpkFR5uPSChGwbBD2Ay07QRrOShU0O/CI+5rSB1PdMxPUcy9DXxWMKbC7Gcgf8IRt3FKqPgK7NVS9PywG9hc72BLgwOFQuCGsbl9Ykr89iYna6s72NpDVPngrAQGn2ZR5UdCgkHNAzNLeeKLndTZfLy4pIL7ppegUp45x9gdO1ukaPOcRANxuq5C2+++epkVrVv5zYQfMKlg5FG1u9G+G4D+Oin5oxiJ4FsjJersTZuWOXPmcPvtt+Pbm5jNYrHw9ttvc8EFFxxxGzfddBOPPfYY7nYntuY2HG4HiQPKeq2P+1BolMRPy8XxeRURqx/PiiZMk7MQBAFNdhy6fhZUKQZ0ReZe37coirjcW7C2f4XHs6uzXK/LJRzqT2nJPWi1vSvu+P1+ampqqKqqorq6ust7VVUVHo+ns65Op+OHP/whDz/8MBkZx2ZXtWnTJu69916WL1/eW4fQK6xatYrhw4fz6quvcsUVVxy2vs1m4/e//z0ADQ0Rhgy+CEQbrW2fk5vz/RPdXQSFgH5QMpq8eDyrmgk3evBtbCNY7cQ0IRN1at8SATVFhQTa2wlWVHYK52VGPcvtHsq9+2ccjMkYQ6OnkaX1S4nXxnPLwFv455Z/sqx+GVmmLCZk9vH7h1OI1W/lhQ0v0BHoIFmfzK0Db+XNnW/S4m0hXhPP3UPvJt2YzvC04Wxo3XBWR50D9Jt4Lo7WZnIHD5NFcxkZGRkZmTMUWTiXkTmLEEWR9jo35atbcbZL4pNKq6RwWAoJqd378B4KpUqBOc3QrdB+EPuizQungOHobBGExnWgSwB/B2jjwNd+1H09qQRcsOVdaXnItaBLQKd2k5agY1iOmUzz0X/Wvc38bS18sK6+82+1UkG/jLhOsfxM9/5Oi9dx33mlPP3lLnY0ufhkcxNXjDizo+Z68jePRCJ81vQNYSHI/cv/zO8Cd3FR/yNLtvjJ9iX4cCHEBH5SPBOAwPbtxNxuFPFx6AYe/9T1UCjEQw89xAsvvNBZNnz4cD766CMKCgqOqi2NRsMvfvEL7r77bh6e9zS7DC288pOJx93H7lDGSzYazgW1BKudaPLiOpM3msb1fjLjaNRPh30lVusiQsG910hBICF+BMkp09Go89i9ex4KRe9YEzU0NPDiiy/yzjvvUFdXd8TbBQIBnnvuOV5++WXuuusufv7znx+xgG61Wvn1r3/NK6+80ultD5JVz4QJp04MFUWR1157jaqqKhwOB1deeSX33HMPf/7zn9Hper6W/uEPf8DhcABwyy23MHTonVRUPIHdvoq01AuPKuq8uz5Fox5CISuhkI1Q2Ca9h2yEwzYUCh0Z6ZdjMpWhjNMQf14uoWon3jUtRB1BArs7+qBwXkRg1WqCFXs6y0qN0iy4Kn+QcExEvXfW2yVFlzAmY0xnUtALCy/k86rPeW/Xe6Qb0zsTicrsp93XzvMbnscRdJBiSOGWAbfwxo43aPO1kaBN4N7h95JmTANgeOpwVjWtYlPbJho9jWdt8lWFUsn4q27oE8EQMjIyMjIyMicGWTiXkTlLCHjCbFxYS0eTFwClWkHB0BQKh6Wg1p5gm4qOamjeBAjQ/4AESrZKaNp4gMf5dzzPNWYAxLKLwLoLBlwC1V/DpnekyPO88Se238fK5nch5AVzHpTMAKAsPY5HLxlITBQPs/HxIe5t/1APcQt3tHaK5hOKkxmTn0hZ+pkVVX4kFCQbuXVCAf9YVsmS3e1cOCQDrerMtGwRRZEde/3NB35HOF/fWkFYEQQRwkKY36z7O3a/m5tGXHTYdt/YPg9ESHcbSPzH28SeGYZ35UoAjGPHIhyl3/N3aWpq4pprrmHFihWdZbfeeisvvfQSev2xDUB9//vf5w9/+AONjY3888P/8KPNP2Xo0KHH1c+eUKcbMY3LwLNS8pDeJ5z3JsFgG1brYjrsK4hFpahbhVJPUuJkkpOnoNFIM4HC4fChmjli1q5dy7PPPssHH3xAJBI5ZF2VSkVeXh6FhYUUFBQgCAJvvPEGfr+fQCDA888/30VAz8zsfkAhEonw8ssv8+tf/7pTaAYoKSnhueeeY/bs2b1ybMfDT37yE+666y7ee+89AF566SVWrFjBe++9R1nZwbMaKioqePHFFwHQ6/X84Q9/wGjMIi5uEG73tqOKOo/FIrjcm/G4d+0VyCWxXIx1nyB0H5WVz5CYOJGMjKtQqUxoC82os0z4NrZjGJbSWU+MxDqthk4lmqIiAEK1tYihEIJGQ7pGTbxKiSsSpdof7EwYqlQou4i5k7ImUeeqY6t1K//a8i8eHv0wZp35VBxGn6TV28oLG1/AGXSSZkjj1oG38uq2V7H6rVh0Fu4bcR/JeulasrR+KR+Wf9h5nzGvet5ZHXV+4P1WwOvB73ZhSe/9wVEZGRkZGRmZU4MsnMvInCVo9EoCnjAKpYK8wUkUjUhFqz9Jl4Ad/5Xec8eBKW1/eftu2PpBj5sJE38mLeji4fzHQRAkYb18Pnz7NylyPaX3bRaOC1slVC6Wlkf9AA5IRKY+wVYg77zzDo888giZmZnMnTu3WxFqya423l0jRYdeNDSDy4ef2VHWh2NUnoUPTRpsnhBrqjuYVJJy+I1OQ1pdQezeECqlQElqV3/zpc07UClVGDATDoXwK1z8Zdsb2P0u7p14Q49teoM+qoN1CKLIpBY1+mFDEUMh/Nu2A2Acf3wDW19//TXXXHMNra2tgBQt/te//pU77rjjmKP7RFGE1iA/f/jn3HvfvYAU9fvBBz1fh44XXakFhVGN0IsDlKIo4vHuxtr+FS73Ftg7YKbVpZOcPB2LeTxKZe8leYxEIvz3v//l2WefZeXegZF9qNVqRo0a1SmOH/ielZV1ULLMRx99lKeffpqXXnoJv99PMBjkhRde4B//+Ad33HEHjzzyCFlZ+wXPJUuWcO+997Jt27bOMpPJxG9+8xvuu+8+NJq+kSA6ISGBOXPmMH36dO69914CgQCbN29m5MiRvPjii9xyyy1d6v/85z/vHMx48MEHO485Pf0S3O5t2O3fkpY6G6027aB97SMcdmDrWI7NtoxI2NltHZU6AY0mCY06SXrXJKNWJ+JybcFmW0pHxwpcri1kZl6H2TwahVaFaVzXGQDurxsAMI7N6JLo9mSjTE5GmZBA1OkkWFODrrQUQRAoNehY5/JS7g10CucH0uBu4JUtr1BmKSPTlEmTp4lXtr7C/SPuR608e5JD90SLt4XnNzyPO+Qm3ZjOvSPuRa/Uk6xPRkTk3uH3kqRP6qzfL7EfAgLRWJRoLHrWR53vw9nWwor33kJQKJj2/R+iNfStGRsyMjIyMjIyx4YsnMvIfAdRFGmucJCUHXfyhOUTgMvqp3abjYGTMlEoFSiUCobNyEVv0qAzncQHRVcT1K2WlhOy4PMHYNBVkD8RErKh+DwI+yDs7/oe8iFqjPvb2SeUjbgFvFZoXAdfPw0z/wBx6SfveA6FKMLaVwER8idBaj/+tbyKnEQD0/ulnjAPbb/fz3333cc///lPAOrr67n88stZunRpl6jcZeXtvLWqFoALBmdw2bBT+5AriiItLS0kJSWdMvFLoRCYWpbKh+sbWLSzjXOKk8/IKdc790abF6eaDppZsK5tO2I0RrFdy3T1QP6u34pHYePfVXOJqdX8dEz3yfT+uupdooRQR1VcaVVjvHECvjVrIBpFk5eHuofo4cMhiiLPPfccDz30ENFoFICcnBw+/PBDxowZc0xt7iPc7MW9uJ7rsmbwRHoGzS3NfPjhh2zfvp2BvWAr0xOarN5JkBqLhbDbV2O1LSLg358QMy5uEMkp04kzDezV89fpdPLqq6/ywgsvUFtb22VdcnIyd999N/fccw/p6Ud+DU5LS+OZZ57hoYce4plnnuHFF1/sFND/9re/8corr3DHHXdw00038ec///mgQY2bb76ZJ5988pj90U8kgiBwxx13MH78eK699lp27NiB1+vl1ltvZdGiRbz00kuYTCa++eYb5s6dC0B6ejoPP/xwZxsGQwFx8YNxu7bS2voZubm3ddmHKIp4vXuw2ZbgcG4AUbKsUaniMFvGotNmoNEko9EkoVZbUCi6v7bGxw/GYhlLfcMbBAPN1NX9E7v9W7Kzb+ycpQAQcQQJNXggJhJu9mIYmYauzHJKrpOCIKApLsK/fgOhykp0paWAZNeyzuVltzdAd/Nk7AE79oCdb5u/ZXbBbBxBB3WuOt7Z9Q43D7j5jLzmR2IR/BE/vrAPX2TvK+zDH/HjDXv3l4d9VDor8YV9ZJmy+PHwHxOnkQZX7xpyF76IjwRt15ky6cZ0RqaNZF3rOsIxafDnbI86BzCaE1HrdHg6bGyY9z/GXXHdGXluHQvOtlaCPi8mSyL6uHiEwyTz7g5RFLt8nlsXL6B++xZUWi2W9EzM6RlY0jNJSM9Arem9gWMZGRkZGZnTVxWUkTlBVG+2snNFEwmpBiZeVXza3fR67EHK17bQXOEEUSQ+RU/eQClSyJJuPMzWJ4AdnwAiZA6HhnWSkO6RIkjJGCK9eiIcBiq6likUMOEnsOhR6KiCpU/CzN9L3uenilgMQm6oXg4dlZLNzPAb2drg5NtKG6urOxiSnUBGQu97m+/evZtrrrmGLVu2dClfs2YNd9xxB2+++SaCILCiwsqb39YAMGNAGleOyDpl5/bu3buZM2cO7777Lrt372bYsGEsXrwYi8XS4zaV7nae3/ZfJmUM5er84xNPv8uk0hT+t6mJ+g4fle1eilN7R+TsS+yzaRmQ0VUACYaD1LmrQICxVhjftgP69eefCXtwqtv5oHEeut2J/LBs+kFtLmpcByIU2Q3EWZLQlJbi+PAjAIwTji3a3OPxcNttt/H+++93lk2fPp05c+aQknJ8swFEUcS3SfL+1mcn8OBDD/Kzn0mzWv74xz/yzjvvHFf7J5Jw2I7VuhRbxzKiEcluS6HQYrGMJzllOjpt7w4eVlZW8sILL/Dvf/+7S2JPgIEDB/LTn/6UG2+88ZjtckAS0J9++ukuArrP5yMUCvHiiy922pjsY9SoUfz1r39l3LjeSzh7ohg0aBBr167l3nvv5dVXXwXgzTffZNWqVbz77rud5x3A73//e0ymrtec9LSLcbu2YnesJjXtQnTadKLRIA7HKqy2JV0GTQzGIpKTppGQMAKF4uhu643GYkpLfkN7+3xaWz/D7d7Grt2/JSP9UpKTpyMISlRmLeaLCvF820Sk3Y93VTPBKgem8ZmoLCc/F4a2qBj/+g0E91TA3rzAZUbpPNzjCxwkrgEMThnMhUUX8lnlZ3xZ8yWXFF3Cfyv/y9qWtWSbspmed/D17XQjGA1S5ahij2MPe+x7qHXVEhNjh99wLzlxOVxTeg0rmlZwft75CIKAWqkmQdm9vdSsglmsb10vR50fgEqjYfQlV7HszX/RUlFO1Ya1FI3s3fuV0xF7cyPL3noNMSYNhCuUKoxmCyZLIsbERHIHDiUhtevMGlEU8btdOFqacbQ0YW9pxtnWwow7f9wpiotijKDPS9DnxWvvoGHn3llJgkBcYjKTb7y1M2Frd9cFGRkZGRmZI0UWzmVkDiAWE9mzThJ1ne1+opEYKvXp4Xnsc4XYs66Vhl32zmn7GcVmkjJPgVi+D68NapZLy+lDYMProFRLUebHg1oHkx+CBb8GdzN8/QxM+5XUdm8RCUHAuf8VdHb9O+CUkoAGnBB0Awd4lw++mrAmgXfWSDfxM/qnnRDR/J133uHOO+/E65WENL1ezyOPPMKf/vQnfD4fb7/9NoMGDeLcq+/gtRXViCJM65/KtaNzTvoDRF1dHe+++y7vvvsuGzdu7LJu06ZNXHfddXz++eeoVAf/LFV7rPx4+VO4gjZWN37Nh+uyeOPSR9GqeyeiyKRVMaYgkRUVVhbvaj3jhPNYTOyMOP9uYtB3Ns4jEPKgFNTMHjkN4Yt5jNldjliYzwfZZppDNby54y2cQTc/H3JZ53atfhd+fRC1R8HlzWqMk8YSaWwk3NiIoFZhGDXqqPu5e/durrjiCnbs2NFZ9otf/ILf//73B9l9HAvhFh+RNh8oBQyDk7lr0F088cQTWK1W3n33XX77299260V9KvF6q7Bav8LhXN8ZWazWJJGcPJWkxEkolb1nBSCKIkuXLuX555/nk08+6cyXsI8LLriAn/70p8yYMaNXrx+pqak89dRTXQT0fde0feufeOIJbr31VhTHEKV4qjAYDPzrX/9i+vTp3HXXXbjdbvbs2cPo0aM7E5sOGjSI73//YB9zKep8CG7XFpqbPkCjSabDvrLTw15QqLGYx5KcPBW9Pve4+qlQqEhLu4gE8ygaGt7E6ymnqekD7PbVZGffhMGQjypRR8IFBQR2d+Db0EakzY/j0yr0g5IwDEk5qf7n2pJiAIJVVYixGIJCQa5Og0Yh4I3GaAyGydYdHGV/ft75NLob2di2kYV1C5mZN5Mva77k44qPAU478TwYDVLpqKTCUXFIoVyv0qNX6TGoDRhUhm7fTWoTSfokXtr0Eu6QG51Sx5ScKYfcvxx13j3mtHQGTZ3Blq/ms23JQpJzcklI7SOzIk8R4WAQjU6HKIpEQkFi0QhuWztumzSQnZJb0CmcN+7eyc7lSwj5fQR93oPacra2kJyTB0DB8FHkDBhMOBjE3twkCezNTfjdToI+L2rd/vvudZ/Oxd1h3RuZnoklI4v45BQUvXBvISMjIyNz5iML5zIyB9BS5SQSjIIgMOvOQSj7QDKswxGLxtixopm67TbEmCR0pObHUzomnYSU3hdrj4pdn0EsAqn9oX2nVJY/WfIsP14MiTDl57DwN9C+C1b9XYpE70nQEUUIeboK3p2iuAv8jr3iuAsCDogEj7JDAmhN0rGWnM+X21tocwVJMKi5ZFjvJon6rjULQP/+/fnggw8YOHAggwYN4sorrwTg0RffYrowiLS0NM4tS+GGMbknTTRvbW3lgw8+4N133+2S3HEfgiCg1WoJBAIsWLCAhx56iGeffbZLnU2NO/nxyr8gqiKoFTq8Xie7xXJmvPdj/jnzl5SlFvRKX6f1S2VFhZV1NXauHR0mQX/m+N7W2Lz4Q1H0GiV5iV2F1oXVawGIi5rJveQSPBYLvDOHkdW1EM3h0/wSqkMVvLvtbdbv/pZ3Lv8jKpWKOVXfIIpRkkMmJnjUGMaNx7NkCQD6oUNRGI9uwG7u3LnceuutuN1uAOLj43n99de57LLLjv8D2It/s/SQriuxoDCoMaLmwQcf5JFHHkEURR5//HFef/31XtvfsRKLRXA6N2C1foXPV91ZbjSVkpw8jYT4YQhC7z3s+/1+3nnnHV544YWDZq7o9Xpuvvlm7rvvPvr3799r++yOlJQU/vSnP/Hggw/y5z//mc8++4xZs2bx61//moSE3k+qerK4/vrrGT16NNdddx3r16/vFM0BnnnmmR4HhaSo8y24XPv/JxptCslJU7FYJqBS9e6guE6bTlHhg3TYV9Dc9AF+fx17Kh4nJfk80tIuRanUou+fhCY3Hu+qZkL1boJVTgyDT25eCHVWFoJeh+gPEG5oQJObi0ohUGzQscPjZ7c30K1wLggC3xvwPdr97TS4G9hu3c7UnKksqV/CxxUf44/6ubDgwj4blRqIBKhyVrHHLkWU17nrDhLKLToLpZZSSiwlFJuLSdQlohAOfx/b6Gnkrxv/iifkIdOUyci0kUfUJznqvHsKR4yhrbqKlspy1n7yEVNuvgNVH8nFcCpIzS9k+m33SGmKtDp8Liceewdeewceu434lNTOum5rW6egLigUxCenYk7P6LRhiU/ZH5kel5jcZR/7CHg8+FyOLt/ljqYGfE4HztYW2LwBkCLfE9LSScrOYfDUmSfs+GVkZGRkTn9k4VxGZi+iKFK1dxp/yajU00I0B1AoFXgdQcSYSFK2ibKx6afGkuW7BFxQ8ZW0XHAurP6HtFw2q/f2Yc6Fcx4gvPhxPDuXYFHpIC4NAi42V9RCwEmCwk8cXoyiD41CRHE0D8UKFejMoEvY+4rfv6w9YFmXIFnF7E0EavME+WxzMwDXjMpB14uzFnbv3s3VV1/N1q1bO8tuvfVW/va3v2HcK1ZeccUVPPbYY/zh5XeIG3M5GzZs4EdXncdN4/JOuCjgcDj4+OOPmTNnDosWLeoiEu1j1KhRXH/99VxzzTVUVVUxffp0IpEIzz33HIMHD+YHP/gBANtbKrhr8eME8aLXmnll1u/4z7fvM6/ta1yije99+X/8bPBNXDfsguPud36ykcIUI1XtXr4ub+fiob072HEq2WfT0j8jHoWi6/+/LtgGAhQqJDsn06RJkuj9738zpL4BMZLJZ7kF7GYnu6N7uOyDn/HRVU+xtPFbxGiM8e5ENEVZqBIt+NZKIrxh3JHbtEQiEf7v//6Pp556qrNs4MCBzJ07l9K9/sW9QbjFS7jFCwoB/eD9D9v33HMPTz31FB0dHbz99tv85je/oaioqNf2e7S4Pbuor/8P4ZANAEFQYraMJSV5+nFHFn+XxsZGXnrpJf7xj39gs9m6rMvMzORHP/oRd911F0lJST20cGJISUnhySef5Mknnzyp+z2RFBcXs2LFCh555BGee+45AGbNmsX555/f4zYGQz4Wy3jsjlXExw0mKXlqr3vYfxdBEEhKPIf4uCE0Nb2Lw7GW9vaFOJwbyM66kfj4wSiNauKm5RCqcyNolAhq6V5JFEXEUAxFLybB7baPCgXawiIC27cTrKhEkyt9L8qMknBe7g0wPan7wXmtUssdg+/gqbVP0eBpoCyxjIuLLubTyk+ZXz2fQCTAlSVX9gnx3Bf27Y8od+yh3l1/0CyQRF0iJZYS6WUu6ZLAMxwN4ww6u3ib5yfkE6+RPptyezkrG1fii/iodlbjj/jJicvhx8N/jFF9ZPeQ+6LOd9l3EaeOo9nbLEedI32PRsy+lMWvvYzbZqV81TcMmDztVHfrpHOgPcqBiVKNZgtGswUKDv6tzRs6AktmNmqtloTUNJSqow9i0JlM6L5jfzXphls7o9L3RaaHgwHsTQ2ds7n2sWHeJ6i1Oik6PSMTo/nU5HSQkZGRkek7yMK5jMxe7C0+nK0+FEoFeYOkhw+vM8jOFc2UjUsnLvHke3l2RzgYpXpLO3mDkjuTl/afkEEokEpydh+ymCifD9EQWPLBUQ+IkD5YErt7kVDKID4UZjHANofIpi9IiZPsOzTNLoKRGA7AsbeuAKAxoDZaGFCQ0ymGB1TxaI1mBL25qyiu1vccwX4I3l1bTzgaozQ9jrEFib1ynABvv/02d911Vxdrlpdeeolbb731oLoX3fJj5rYl09jcgqd6I/98+D/cOWX1cftEd4fX6+Wzzz5jzpw5zJs3j1AodFCdAQMGcP3113PddddRXFzcWZ6dnc1LL73EnXfeCcAPf/hDSktLSS3L5raFjxHEiwotj4+4i9L4NB4//yeM3NqPP236DyH8/Gnrq6xr3skzFzxw3McxrV8aVe1VLN3dzgWD0k9YMteTTadNS0ZXManNbcMjOBEQOXerlajTiTIhAcOIESj0eoSX/8GYQQNReb18ERDYpt5BQ6Se8967BzcO1Godd3z//0gKgX/rVmLe/2fvvMPbKu82fB/tZVnee+/Y2WRvkrBnmWGnUEYpI4XS0sJXVqEUygqUvUfYI2wIBBLI3o7jvae8ZGvv8/0hR4ljJ3ESJySg+7p0ST7nPUPH0tE5z/u8z8+G1GBANWJozuT29nYuvPBClvc51QEuvPBCXnjhhWAn0HBh37bTbW5Aqt11Ix4WFsaiRYu488478fl8PPDAA7zwwgvDuu2h4Pd7MRqX0t7xFYgiMpmeqOjZREXOQi4fhhE6u7F27Vqeeuop3n//fbxeb795kydP5qabbuKcc85BLv/1jLo4GlAqlTz66KOcccYZ/Pzzz/zxj3/c7zIpKVeQnHwxEsmRLXQnl+tJS7uaiIgpNDW/icfdRW3tExgME0hMvBC5XI8yrf/n0lluwrGlHe3EBBQZ+sMqNCmzdwrnVYQdPweAXE3gGFXYnftcNkodxVUjr2JZwzJOTD8RjVyDWqbm3fJ3+aHxB1w+FwvyFwzJqT2cWN3WoEhe3VNNs6UZkaEL5QCNlkY2tG2gwlRBk6VpwPLXjr6WougiIFAwdYNxQ3Bemj6N68dcj0Z+YPFPv8v9HUqpki5HFw+sfSDkOu9DqdFw3Gln01iyjdzJ03/p3Tni+H0+Vr33JumjxpFUMPQOP7UuDLVu+GsWafThaPThJOUFrk9EUcTWY6KntQVht1E/Pq+Hhu1bEXczfciVKiISAhEvMWkZ/dztIUKECBHit0FIOA8Roo/avmH8ibkGlJqAYFC2uhVjbS8+r5+Jp2f8oo4Dr8dH3bYuaja343H58Lr8jJgecMXqo3/hSJY98TgCwjlA3qmw8eW+14fuDN4dURR5fmUNm9yFWCLP4KKULtBHgiocaYIEq0dJm1tJs0NBk0NOj0+DX5CSEa1lxPwRwfXc81Ex3VY3cXolceFS4vU+4vV24sL9xOlV6JRDP1W2m51saQwMER2uWJTBollGjBjBu+++S2Fh4YD2xU29PP1DNaPHjMXVsoTS9Z9gQeTcc8/l22+/RTEMQ4bdbjdff/01S5YsYenSpf0yiXeSkZHBhRdeyIIFCygqKtrrsfjDH/5AcXExixcvxuPxcMHVl5L8txm4BCtSFNylP4Fp8l0ZoSd0yBg58gauKXkZk7+DZZ0/cebbTbx+1j3oVQffeXRcegTvrJfRY3ezpbGH49KHr9Pjl8Ll9VFpDBR33DPf/KX1nyAiovCrmKmJRbpbHIaqoICEu+9CajAgqaiADaByytkg34ZFNAEQ7tWTEB7oiDG9EyjmqZk8CWEIOdRr1qzh3HPPpbk5UORQJpPx3//+lxtuuGHYz7Oixw9+cYDbfCc33HADDz/8ML29vbz66qvccccdpKenD+s+7AuXq536hudx2OsAiIycTmLihUilwyeWut1ulixZwr/+9S8qKyv7zZPJZJx//vncdNNNTJwYKmR3uJkzZw5z5swZUltBkCAIR1Y03x29fiR52rsxGpfS0fktPT3rMVu2k5h4PpER04LfVVEUcVX34Hf6sKxoQl6tQzclAanu8MRTKPs6X11VlUFXa7ZGhUSATreXLreXKMXef7d3is87mZE0A6VUyRs73mB1y2qcXieXF16O7ACLrR4Iva5eqnqqqDIFxPI2W9uANrGaWLIMWeQYcsiOCESv7MTusVPcUUyiLjEooLdaW/mu4btgG4kg2ZVlLtMgl+zqDEvTp3F2ztloZBrCFGHkReb1mz9UdjrYE3WJjIkdw+b2zSHXeR8xaRnEpA1PnNyxRvmqFXTU19LbbiQuMxu56ugwH+1EEAR0EZHoIvpf54kijD3p9KArvbe9DY/LSXtdDe11Ndh7e4LCuSiKlK9eiSE2npj0jINyx4cIESJEiGODkHAeIkQfWWNjESQCGaN3CSv5UxIw1prpbLRgrDMTn3Hkc1Z9Pj8NJd1UbTTitgfcgboIFZG/ZNHP/VG1DNw2CEuA9GmBPPKG1ZA4blg3896GJjbVm5BKBGaffhlh8btcKrl9j52Iokivw0Ob2cnuo539fhGTzY3H56fJ5KDJ5Oi3jdQoDf88fZc4/UN5O2EqGbFhKuL0KhR7RPrE6lXcdUYhlUYLKZGHXrivo6ODefPm9cseXrhwIYsXLx7UlVvS0suTyyvx+UUmZEZzz2N/YdKqd2lra2PFihVcf/31PPfccwclTvp8Pn744QeWLFnCBx98QE9Pz4A28fHxXHDBBSxYsICJEycOeTuPPPIIpaWlrNy2lpibJuAQzchR8X/6+Yz+ch0dPxYT949/4O3ooOe991BLJXx07kVc3/kNJY5SGvyNLFz5Xx6Z8ifSdAcXLSGXSpiZG8Pn21r5vqz9VyGcVxqt+PwikVoFsWH9Bbg1baUIQJRdR8SIgR0wUoMBgNzcXOw9vbiX1aGJHcNK5VZE0csJSRMA8PX04CwpAUA7Zd8xLaIo8swzz3DTTTfh8QQKysXHx/Pee+8xffrhceUJcgnhJ2XgM7sGFfLCw8O56aabuOeee/B6vTz44IM8/fTTh2VfdkcURUw9a2huehO/34VEqiYl+TIMhgMvrLo32tvbefbZZ3n66adpbW3tNy8mJoZrrrmG6667jsTEX080UYjhRSpVkph4HgbDRJqaXsPhaKCp8VVMptUkJ1+KShmPIAiEn5SBY3sn9q0deJqt9HxchWZsLKqCKATJ8HaGKdLSQCbFb7bg7ehAHhuLSiohXaWkxuGiwu5kimLonahf1X2F2+fm90W/55WSV9jcvhmXz8VVI69CIR0e8b/b2R0Uyat6quiwdwxoE6+NJ8cQEPWzDFmEK3ddc9o8NrZ1bAtknPdUBh3pZ2Wfxby0QMH13IhcpiZODWacG5SGvf4Gx2vjidcOX9FKURRJ06exumX1UeM6t3vs+EQfYYrhdzAfKKIo4nY4+kWW/FrpMbZRviZQ12b0CaccdaL5vpDJ5aSNHEPayDFAwDlv7mjH1Cekx6SmBdtau7soXRkYMafShTFixvGkFo0aknkgRIgQIUIcW4SE8xAh+jDEaRh3Qlq/adpwJZljY6je2E7pTy3EpIQd0ezzxrJuKta24bQGBCaNXkHOxHiScgzDfiM6bPg8UPZ54PWIMwK53/FFgccw8n2Zka9LAg6t30/PIC9+3zdGgiBg0CgwaPrfBEskAosXjKXL5qat14nRHHi0mZ0YzS7i9bsu+P1+kSXrGvD6dinvkVoF8eEqYvUqcmN1TMqMIsmgJskwPKMAbr/99qBortFo+N///sfll18+oJ3d7eWzra0sKzXi84uMSTFwzcxMZFIJH3/8MbNmzcLlcvHCCy8wcuRIbrzxxiFtXxRF1qxZw5IlS3j33XcxGo0D2kRERHDuueeyYMECZs6cuddid/tCJpPx9Ksvcv5nt+FVOhH8UopWORgrCwwl10yejDQyEolGg3r0aBxbtmB5+20WT5vG88mZfGFeT5u1nit/uJu/jb+WeQkj9rPFwZmdF8sXxW2Ut1loMtlJjji2b3J35puPSBwYndDq7QRBJM8kQTV93/Eqydu2YezsZIxdS0zqJBztlVxUVYt3tAn7uvUgiiiyMpHHxe11HXa7neuuu47XXnstOG3GjBm88847JCQkHMK7HBxvjwtBJgTFcql+787dm266iUceeQSr1cqLL76Iw+Fg3rx5zJ0797Dsm89np6n5TXpM64BA4c/UlCtRKIans2bLli08/vjjLFmyBJerf6HjUaNGcfPNN7NgwQJUx5CgEeKXRaNJIyfn73R2fkdr2yfYrBVUlN9FbNypxMacjEQqQzM6BmW6HuvqFjxtdmzrjbhqetFNS0I2jHF3gkKBIi0Nd3UN7qoq5LGB4oK5WhU1DhflNidTDEMTzhvNjXxeE7hmmZE0g2tGXcPzxc+zo2sH/9vyP64ZfQ1q2YH9nouiSKejMyCSm6qo6qmi29nd/z0gkBSWRI4hIJJnG7LRDSL2dzo6eW7bc7RaWwdEr8RqYvu5xA0qAxcVXHRA+zpcfFL9CcvqlyHtq/VyOF3noihi99rpcfXQ4+zB5DL1e93r6sXkNOHyBc59BVEFzE+bT44h5xcZOWrrMbHpi09w2e3M/f21v2ph1e/zsemLTxD9PhLzRpCcP7BT/lhCIpUGi5NmjNmjaK4AqUWj6aivxWExs+nLT6jasIaiOfOJGyS/PUSIECFCHLuEhPMQIfZD9vhYmspM2M1uard2kj0+dv8L7QNjrZmSlc0IAggSAUEQ+p4Df+dOjCO2Lzu0t92B0+pBpZWTPSGOlPwIJEd77nLNj+AwgSYK0mcelk1saezhrbUNAJw9LonJmYdWvE4mlRCnDzjI92T3Ylxun58J6ZEYzU5ae5043D66bW66bW52tJixOD1MOsR92Z2ysjJefjkQc6PX61m9ejUjRvQXhL0+PysqO/h4cws2V2BEwvj0CP4wIzOY0T1p0iRefPFFLrnkEgAWLVpEfn4+J5xwwqDbFUWRbdu2sWTJEt5++23q6+sHtNFqtZx11lksWLCA+fPnH3L8S6/bwd9LXwM9CA4p2jcrmFhmoiLHwvir/4DhvPMC3xW1mqhrrsby9Tf0fvIJtp9/5oq0NE6++Gb+vuM1TI5WbvvhbiZqR/PkKbchkx3Yz1ykVsHYVAOb6k0sL2vn0inph/S+fml2tAyeb15v6cIttYNf4IQOAU9rG+YvviTiogUoUlIGrCfi/PMY+UQbLpMJsdaLzG/ALfMjCQ/HtmY1ANopU/e6H9XV1Zxzzjls3bo1OG3RokU8+OCDw56lLYoiznIT9vVtSKPVhJ+Yvt+OxsjISG644QYeeOABPB4Pr776Kq+++ioQiEWaN28e8+bNY9asWej1h5Y5brNVU9/wfKAAqCAhPu50YmNPQTjETGWfz8cnn3zC448/zooVK/rNk0gknH766UycOJFbb711WOKaQvz2EAQpMTEnEB4+jqamN7FYtmNsW0pPz3pSki9Dq81GGq5Ef2I6rqoebOvb8HY7YY+ilsOBMjsHd3UNrqoqtFMD555crYqvOnsptTr6FSbcFyn6FC7Mv5B3yt5hZfNKnD4n146+lueLn6eqp4rFmxdz/Zjr91kwUxRFjHYjlaaAm7yqp4peV2+/NoIgkBqWSrYhm5yIHDLDM/vlifc4e9jQtoHKnkqiVFGckB74fQ5XhtNub0dEJFYTG8w3z4nI6edI/6WZnDCZ7+q/w+f34fV7D9p1LooiVo91UCG8x9UTfLh9A2up7I3SrlJKu0pJ16czP30+o6JHHVEBXaFSY+7swO2w01CyLehm/jVSvnolve1tKNQaxpxwyi+9O4eVsMhoxp96Fj6vl5pN6yhftRJzh5FV775BbEYW4089C5X2KKo9FSJEiBAhDpqQcB7iN09dcSeWLieZY2LQGgY6EmVyKQVTEtiyrIGqDUaS8gyoDzC3U/SLQeHG6/bhsOz9gt/t9AVfZ4+PRaNXkFYUdUSd7geN3welSwOv80+D1YtBEw0Fp4PaMGybaet1IIowIyeaU0cOvxt0d3a/uVLJpVw1Y1e2odXl7XOou2jtdZIcMbxZ83feeSf+vgJFt912Wz/RXBRFtjX18u6GRtp6A8XQEgwqLjgulZHJA2+mL774YkpKSnjggQfw+/1ccMEFrF27ltzcXYE2lZWVLFmyhCVLllBWVjZgHUqlklNOOYUFCxZw6qmnohmmIcc2j4trfnqUZnMNek0EZ/Smoixbh4DAK1u20C2RcP5u/wdBENCfdCKK1BS6XnwJd309UU+8yIu/v5Tra9+nyrqdNb0bOP+Dv/HuOf8+YPH8+PxYNtWbWF3TxTnjk9HsIyv3aMbi9NDYbQcgfw/h/Mu6jYGMT18YYxPTsSz7Fr/ZQvsjjxB93XWocnP7tZfq9cT/eRET/vc0K41GLFIpXXl5JNXV421tQ5DL0YwfPIrp888/55JLLglG+2i1Wl588UUuuOCCYX/PfqcX688tuBstAAgyCaLXj6DY/yiI22+/ncrKSj7//HMcjl2RTTt27GDHjh088cQTSKVSJk6cGBTSJ0+ePGQRWhT9tLd/QZvxUxD9yBVRpKX+Aa320JxpJpOJF198kSeffHJAJ5fBYOCqq67i+uuvJykpiS+++OIXrdUR4teBQhFNRsaN9PSsp6XlbVzOVqqqHiQqahYJCb9DKtWgyolAkaTD02ZHFrXrt9FndiPVH3rHjTI7C8vX4KqsCk7L06qQCQItLg+fdfRyeqxhSOuanjQdlVTFazteY33bepxeJ38c/Uee3fYsDeYGHt34KDeMvSEoVIuiSIutpZ9QbnVb+61TKkhJC08L5JMbssk0ZKLcrW5Bl6OLbZ3bgo70TkdncF6iLjEonMslcq4bfR0JuoRgnvgvSYfbQ7RcNuA8Eq+NZ1zcODYaN+LxB0ZJ7uk6F0URs9u8SwB37hLCdxfGvf7+RYv3hlauJUIVQbgynAhlBAaVAYPSEHitNGBQGbC4LXxX/x2rW1dTZ67j+W3PE6eJY376fI6LO+6w5tjvRK5SkTtpGtt/+Jayn38kuaAI6QFelxwJ7OZeajcHRvnlTpp2wBErPcY2ylf/BMDo+Sej1BzFkZLDiFQmI2fiVNJGjqFs1UpqN6/H1mNCoTrK6k+FCBEiRIiD5uj71Q4R4gji94tUb2rHafVgiNUMKpxDoGBo/fYuTG02ajZ3UDhj6A6ami0dtNeZmXBaBlKZhJi0MKadm4Moioj+wI2E3y8i+kVEEfTRuy5UVVo5mWNiDvl9HgyVlZX8+OOPACgUChQKBXK5fNBnhUJBTEwMyd56sBpBoYPoXNj0KiBA7knDum8nFSWQEqkhLy7sFxOBBEEgTCUnTCUnO3b48zM3btzI+++/D0BsbCw33XRTcF5jt513NzQG3cQ6lYyzxiQxMzcG6T6ctffddx8lJSUsXbqUnp4eTj/9dD744AO++uorlixZwqZNmwYsI5VKmTdvHgsWLOCss84iPHx4HW5d1h7O/+zv2BU2FFIVtyeey4ivP6JmxAhe3LCeN3tMfLhwIdk5OYwb11+YVY0YQdzfb6fzmWfxNDaiNVl4+8T/49rP7meDZQs17hqu/PQeXj37ngPap/z4MBIMKlp7nKyq6mLeiL3HjxzNlLUFxOPkCDXh6v6u7lUtW8EvkmzXoYyPx15dA4DocNK5+EmirroS9ejR/ZaRaDTE33gDOc8/z2ajEaNOh231KgDU48YhUQ+8SXzggQf4+9//Hvw7NzeXDz/8cNCitoeKu8WKdWUzfocXJALa8XGoRkQO+RwRFhbGe++9h8vlYvXq1SxbtozvvvuOdevWBTuwfD4fq1evZvXq1dx7771kZmby9ddfk91XrHCv++buoqHhRWy2QGFOQ8REkpMuRio9+M6nsrIynnjiCV599VXsdnu/efn5+dx4441ceuml6HQBx9vOTPljAVEUsdlsaLXakNB/lCIIAhEREwkLG0Fr6/t0d/9MV9eP9Jq3kJS0gHD9OCQaOcrMXb8ZXpOTnk9rUKbp0U6MR6I++NsQZVYWCALejg58vb1Iw8PRy6RcnBjFq82dvNfWTbJKwVj90L5jx8Ufh0qm4oXiFyjuLMblc/HHMX/kuW3P0WZr49GNjzI9aTrVPdVU91Rj9/b/zsklcjLCM8g2ZJMdkU2GPgO5NHDe3SkY7y6cP7H5CbocXbuOJwLJYclBR/ru5EXmHexhGjZEUeTVli6+6zJzZqyBc+MHxkqdnHEym4yb+rnOXyh+AbMrIJb3unrxib5B1j6QMEVYQPxWGohQRfR73vnYeXz3hVKt5IL8Czg582R+aPyBFU0rMNqNvLHjDT6r/ozjU49nWtI0JBxec0rGuAlUbViDvbeHuq2byBp/9BViXvfJ+5hamgBo2L6VUXNPIjGvYMjn4I66mkBES24BScd4RMvBoFBrGDX3RLLGT8RpsyLpiy30+3xUb1xL+pjxyBW/XLHnECFChAhx8ISE8xC/adqqe3FaPSjUMhJzDXttJwgChTMTaa+3DFnIFkWRinVGqjYEMqFbq3pIzo9EoZKhUB29X72amhruueceXn/99aBYNFQ23z+fMWkRkH8KVH8fmJgyAXSHLv7b3V4kgoBKHrgQLUw8eoYoHw52FxvvuOMOdDodvQ4Pn2xpZkVFB6IIUonAvBFxnDYqYUiuaIlEwhtvvMHUqVPZvn07FRUVjBw5ctC2M2bMYMGCBZx77rnExAx/543X6+WT0uU8ufU9TGInEq+Mu+f8hXkxuXTmbqdw3FikiQnw6qs4HA7OPPNM1q9fT3x8/2Jmsqgo4v5yK/aNG4OFKV846/+4+pP7WGfexFbrdhZ98TCPnnLrkPdNEATm5sfxxpp6vi9vZ25B7FEh3rX0ONhYb6Kmw4bH58cninh9fnx+8Pn9eP0iPr+I1x/ojLO7AwLFiMT+LkWv10t59w6QCExJGoPEGxCWlLm5SNQqHFu30fnsc0ReesmAYp+CQkHhH/5A+YcfYrHZ6F27FhmgnTqwKOjatWv7fY7PPvtsXnnllUOOOtkT0efHvqkdR0lAhJIalITNTD7oTGWlUsns2bOZPXs29913Hz09Pfz4448sW7aMZcuW9RuNUVNTw+mnn87q1asx9BVT3RO7vZ6a2kfxeW1IJEqSki8mwjD5oD5Tfr+fr7/+mscff5yvv/56wPyTTz6Zm266ifnz5yM5BnN0RVGkubmZ7du3093dTXx8PFOnTg1lsR/FyGQ6UlKuICJiCk1Nr+NyGamvewa9fhRJSRf3y+33GO0girhqe3G3WNFOjEeVZTio7Uo0GuSJiXiam3FVV6Pp61idF6Wn0enm+y4z/2to567sRJJUQ3O4F0UXcd3o63h227NUmCowOU0sGr+IJzc/Saejk4+rPg62VUgVZBmyyDJkkWvIJUWfEswbF0WRVlsrVT1VVJoqqe6pxuP38ODMB5H0RTLlRuTSpmjrJ7TvHt1yNOEXRV5s6mSFKdAZ+2VnL/Oj9ITL+193DOY639K+pV8bAQG9Uj/AIR6uDA8K4+HK8P06wd0+N/Xm+qBTfc8ol7mpc5mZPLNv//20Wls5PfN05qXO4+eWn1nesJweVw8fVn7IV3VfMSNhBi7Rtc9tHgoyuZz8qTPZ8s3nlK9eSdrIMch+wcgsURTpbm5EHxOLXBk4v2aPn0StTIbTYsFq6mLdJ+8Rn5XL6Pknowk37HedOZOmEh4Xjz7m6Lhm+qXQGiLQGiKCf9du2cj25d9SuXYVBdNnkzZ6LBLJgdcCChEiRIgQvxxHr3oXIsRhRhRFarZ0AJA2cv9RKOExGsJjhnZTI4oiJStbqC8ODL3NnRRPUl7Efpb6ZWlqauK+++7jxRdfxOsd2jDZ3TkuUYKxbB2+9FORpk6FL/8SmJF36iHvm9fn56kfa3G4/dw4N3tAgc9fGz/88APffPMNAGlpaVzx+6v4oriVz7e14vQExNDx6RGcOy6Z2EFy2fdFWFgYS5cuZeLEiXR2dvabN378eBYsWMD5559PyiA514dKTWcjr275jA0dZRi97XgJ3KQKSFiYcTYnJAUKyMZcdx1IpTxzySVUVFayatUqmpqaOPvss1m+fPkAEU1QKPoJvD6rlX81RvCnyEzKfTUs71rFv5a/wD/mDL1Q2ZSsKN7f2ISx18mOVvMv0lEjiiL1XXY2NZjYWG8KRvIcCIIAx6X3dwZ+W7kap9eKgMAp42fj/egLAFQjiwg7/nhMb7yBbfUaul99Db/NRti8ef2WVygUJCYmYvr5Z2zdJiIz0lHuEe0C8MILLwRf/+1vf+P+++8/PDfTYsBtDqDKj0B7XDzCMEZbGQwGzjzzTM4880wAmpub+e6777j//vspLy+nrKyMCy64gM8//3xALJDD0UBNzSP4fHbU6lTS0q5BqTzwOhlWq5VXX32VJ554goqKin7ztFotV1xxBTfccAN5eb+8M/Vg2CmYFxcXYzKZgtPb2tr48ssvmT59+mHpwAsxfOh0eeTm/h/t7V/S3v4lZvM2rNZy4hPOJjpqDoIgQZ0fiSxajW1VC95uJ9aVzQgSAWXGwZ1flTnZAeG8sioonANcmhBFi9NNmc3Jo3VG7s5ORCsbmlCVF5nHjWNvpN5cz5jYMQAsGr+It0rfCgygM+SSHZFNsi45WAhzJ1vat7CubR1VPVXYPf0d6TKJjE5HJ7GawPf/ovyLjglx0SeKPNfYwaoeKxIBImQyujxePu/s5aKEgTVddrrOBQQmJUwiSZcUjE2JUEagV+gHHLd+2/P7MLvNgYKfe0S5jI0dy7i4wP+5xdrCwxse3ut6dnfzb+vYxgvFL5Aclsy81Hkcn3I8s5Nns7ZtLcvql9Hp6OTLui/ptHdStr6MpLAk4rXxwceeRVgPlrRRY6lctwpbj4nqjevImzL9kNd5oIiiSFtVBRVrf6a7uZGiOfPJmRioEZBUUEjyiCJ8Xg8Va36mYs1PtFVX0NFQy7hTzhxSoc/Y9MzD/RaOObThBnQRUVhNXVRvXEfa6LG/9C6FCBEiRIgDJCSch/jNYmqz09tuRyKVkFYUfUDLin4Ra4+LsEEcjX6fn63fNdFSaQJBoHBGIukjD2z9RxKj0cgDDzzAM888g8u1y20TERHBjTfeSHJyMm63G4/H0+9599c///wT50WU4Ha7+aldy6yG1eDzQGQmxByakCOK8NqaBspaLajkUixO769aOBdFkdtvvz3495133cOD31bR2hMQTdOjtVw4IYWcuIOPh8nIyOCTTz7hsssuQ61Wc/7553PhhReSk5Oz/4UPAK/Xy/quOr5vK2ZL53aqjdvx+3bFRQhIMAiRLDCM5wKTPljMTehzYSmlUj788EMmTJiA2NbGpKoq/nXmWfz9449QDxILspOe997HuX07D0eEc01hIi1iC+83fU1h1QjOyt57AcvdUcmlTMuO5rtSI9+Xth8x4dzvF6nqsLKpPiCWd9t21UOQSgRGJOoZmRSOVilDKhGQSQSkfQ+ZRNJvmkwqoFPKCFP1v+H/aEcggknrjyAzNpmWykB8iKpgBIJUSsRllyHRarEs+46e9z/Ab7OhP+OMfiJPeno63tffwGG3o5k0aYAAZLPZeOeddwDQ6XTccccdwyoS7SzaKwgCgkxC2KxkfGY3ytSBbnaz2YwoisMWM5SUlMRll13G9OnTmThxIl1dXXzzzTcsWrSIxYsXB9s5HA1U94nmGk0GmZmLkEoPLPO0traWJ598khdffJHe3v7FBjMyMrjhhhtYuHDhXt3uRzuiKNLY2Mj27duDGfgymYycnBwSExNZt24dFouFZcuWMXbsWPLy8o4JsXFviKJIeXk5HR0djB8/ftjqRBwtSCQK4uPPxGCYQGPTa9ht1bQ0v43JtIaU5MtQq1OQR6sJPy0T2/o2nKXdWH9qRhKmQB594HnAyqwsrD/8iKu6qt90mUTghrQ4/lnVjNHt4cmGdm7NiEc6xM9Oeng66eHpwb+lgpQL8i8gUhXohPT6vTRYGqjqqWJq4tRg4dAmaxPbOrYBAUd6RngGOYYcsgxZpOvT+0WLHAufY69f5OnGdtb12pAK8MeUWJRSCQ/XtvFdl5lTosMx7MV13m5vZ3rSdDLCM3Zbn7efEN7r6iVFn0JuRKDjtdHcyH/W/weRwYvJRqoig8J5v9gWVf9c8whlBNGaXdfdva5eFFIFTZYmXil5hc9qPmNu6lwmJ0xmauJUtrRv4auar2hvb6fN1ka7o73fdgVBIEYdQ7w2ngRtAnGaODLCM4jRHFhnnkQqpWD6bDZ89hGtVeXkTp52xD4HPq+Xxh3FVK1bhaWrM7g/nt2u+3fui1Qmp2D6bJILitjy9Wd0tzQRHjt4ZJ3f56P4+6/JnjCln8s6xC7is3OJzciidstGwqKiQ27zECFChDgGCQnnIX6z1G4NuM2T8gwoDyBn02F1s/7zOpxWD7MvzusXu+Lz+tn0dT3tdWYEicCYeSkk5hydF5JdXV089NBDLF68uF8+blhYGH/+859ZtGgR4Rol+D0BEVz0g9+728MXfC7fmEfF67fi8cOdr63kx1QBASDv5IDldTecHh9NJgdKmQS1QopKLkUtl+41m3tjp0CTqRupRMp1s7NIifx1CQ178umnn7JmzRoACgsLiR59PKu2thKmknH+hBSmZEYNy43W1KlTqaqq2n/DA6S6s5FXNi9lQ2cZ7d4OZGo5sj6xQCqTIvPJSVbEMy1xFFeMPQNdawcdi5/E7N6CLCpyQDRIXFwcS194geVXXoVSFKGkhAcnTWbhB++Ttheh33D2Wbjr6vAajTxZHcdV2R4scguPl75JakQS46LShvRe5uTH8F2pka1NPXRaXUTrDk82pdfnp8kGb6xtYGuTGYtz14gPhUzCyORwxqdGMDI5fFgKlVbamxGAFKcWx9ZtiG43krAw5EmJQODmOfycc5Dowuj9+GPMX36Fz2olYsEChL74jzi1mtb2dnx+P85B/g8ffvghFktgWP/555+PVjt8RcL8Li/WVS3IIlVoRgfcmzKDCpmhf0emKIqUlpayZcsWpFIpp5122rDuR2ZmJh999BFz587F4/Hw5JNPUlBQwB//+EccjqaAaO619YnmNw9ZNBdFkR9++IHHH3+cpUuXBjsJdjJnzhxuuukmTjvtNKTSX/YG3O/3Y7VakclkqNXqIZ+b9iaY5+bmkp+fHxxVctJJJ7F27VoaGhrYtGkTHR0dTJo0acgFWY8mHA4Hq1evpq2tDQC5XM7kyZN/4b06PKhUiWRn/ZXu7hW0tL6Pw15HReV9xMacQFzcaUgkSrQT4vFb3LibrFi+b8BwWiYSzYG5ehV9tQU8jU34HY5+dRb0Mik3p8VxT3UL260O3m7t5uLEgQ7p/WH32Hlqy1NY3BamJU2jqqeK2t5a3L5Ap2asJpbRMYF6EKNjRqOQKMiJyCElLOWIFJ48XHj8Ik81GNlotiMTBG5IjWVcuBZRFMnSKKm2u/i8o3fQY3pRwUUoJAoEQaDD3sFL21+ix9WDxW0Z0HZu6tygcK5X6hERkQiSAfnmBqWhX2dGuDKc+6bfN6T3MitlFsfFH8eKphUsb1xOp6OTd8rf4YvaL5iTMoe5qXMpiijivbb3KBpVRKezkzZ7G63WVtrsbTi9Ttrt7bTb24MdI4IgcGXRlcFRCUMluaAIQSolMTf/iInmletXU7VuNU5r4PjLlSoyxh5H1vhJqPpqYAxGWFQ00xdcjrnDSFjkro6IprIS4tKzkKtUVKz9mZpN62mtquCEq28I5nqH6I9EKj0qc+1DhAgRIsTQOHav6EKEOARsvS7aagKFFTNGH5hjRKmWIfpEPE4vleuN/QqFOiweulttSGQSxp+URmza8Gb5Dge9vb08+uijPPLII0FhC0CtVnPjjTfyl1tvJcpZB+segY6yva9oN/KAjshI3lrXDp2VdDYmEpOcDakD3b1P/1DN9ubeAdPnjYhjwcRUAKwuL0//UIUEkU1dArGxcNmUNIqSft255j6fr3+2+d338U1pwPm0YGIqkzIP/Kb/cOP1enm3+Gu+rF1Dja0ZGz395kt8UrJiRnBcTBEnJI4i35CIz2rFuWMHzvc+pmPzZkS3G9WIAjTjxw9Yv6elhZhvvmXGccfx5bp1GIDY7m7enD+fKf/9L3POOWfAMlKDgZgbb6D9Pw9Bq5HntVncNspOm62Bv655jGdm/p2ssP1/7xPC1YxI1LOjxcwP5R2cOz75II/SQFxeHyUtZjbVm9hcb6KuWUKsqxOJEOhQGpNiYHxaBIWJ4SiGMXbE5rLTgwkQmdToxKneAYAqv/9NvCAI6E86EYlWg+mtJdhW/oTfZidq4RUIcjmuDRtQq1R06bQ0Wq3E77Gdl19+Ofh64cKFw7b/7lYb1pVN+O1ePE1WVHmRSAapGeF2u1mzZg1NTYFCZz6fj/Ly8gEFZg+VGTNm8NxzzwXf44033khubiTxCWv3EM2H1uG3Zs0arrvuOrZs2dJvulKp5JJLLuHGG29k1KhRw/oeDgRRFOnp6aG9vR2j0YjRaAwWHZVKpWi1WnQ6HTqdbsBrhUKB3+8PCuY7HfQymYy8vDzy8/NRKvt3TsnlcqZNm0ZMTAybN2+msbGRnp4epk+fTkTE0dkpPRgtLS2sXr0al8uFVCrF5/NRW1tLYWEhYWHDX1z6aEAQBKKiZqHXj6a5+W16ezfS3v4VPT0bSE6+lLCwEehmJtP7RS2yGDWC8sBvSWQREUijo/B1duGqqUG9R9HhNLWSa1NieaLeyFedvaSqFMyIPLDj7fa5cflc9Lh6+Lzm8+B0jVxDVngWGtmu73ZKWAopYcMfc3akcfv9PFHfzlaLHbkgcFN6HKPDAu9TEAR+FxfBQ7VtfN9t5tSYga7z3YugKqQKGi2Nwb9lElk/UTw5bNfvql6h51/T/4VeoR92UVkr13Jyxskcn3o8q1tWs6x+GT2uHrZ1bGN+2nz8gh+dRMeIyBHI5bs6cHYWdm21tdJma6PV1kqDuYFGSyNvlr5JSlgKUeqhX5sJEsmQIk+GE1NLM06rBXWYnqzjJpE+elww13x/CIJAeOyuX/julmbWL/0AlUZL9sQplK9aAUDhrLkh0TxEiBAhQvxqCQnnIX6TyBVSssfHYje7B41b2RcSqYQRMxJZt7SGuuIuUkZEoo8KuJx0EUomnpaB3y8Slbh3F8cvgc1mY/HixfznP//plyGrUCi49tpruf22W4h3VMDq+8DSusfSAkikIJHt9pD2e44skPL2S0uQSQTe3mzmhvmng7T/Kaax28725l4EAcJUchxuHx5foACpQrpLHLS7vJS1WvCLgXmnFMUzM/fXn2/71ltvUVJSAsCkSZMQUsfhLDGSEqlhYkbkfpY+crTaevimtZg1xmKKjZux2rr6zVeiJUkZz8zE0Vw+7gwiNLs6kDoWP4lzx45ABs/O9gX5RF97bTCiZSeetjbaH3sMv9VKysQJnPG3v3LjRRdxkt2BAdhy401UrV7NVQ89NOAmWxYVRfQNN9DxyH9RVNVyX1ght6Y66LK3ccmnf+HV+XeRG5O+3/d6fH4sO1rMrKjo4IzRiYckYtvdXrY19bKx3sT25l7c3sDn2y/6UUlhZk40EzKiyY8PQyY9PIUd397yJX58SEU5J3nD8LS0AKAaUTBoe92MGUi0WrpeegnHpk10OOxEX3MNtlWrUWvUWDIy6aqvZ9y4cUH3c21tLcuXLwcgJyeHadOmHfJ+iz4R++Z2HCWdIII0XEHYzORBRXOTycTKlSuxWq1IJBLS09OpqamhqqqKoqKiYXcrX3HFFezYsYOHHnqIqCiBn366lfknzCAmpmjIornP5+OBBx7grrvuwufzBacnJiZy/fXXc/XVVxMdfeQjv0RRxGKx0NbWFhTLd4/0AoJCsM/nw2w2YzabB12XQqFAKpXicDiAgCiel5dHXl7eAMF8dwRBIC8vj6ioKH766ScsFgvffPMNEyZMIDPz6M7T9fl8bNu2jdLSUiCQlz9t2jQ2b95MS0sL27dvZ8qUgYV1f03I5QbS06+lt3cLzc1v4XZ3UlPzKBERk0lMPJ/wUzIQ5JKDFkqVWdnYO7twV1UNEM4BJoRrOSvWwMftPbzU3Em8Uk6OdujXfQaVgUXjF/FO+TsICOREBKJXErWJx0TcyoHi8vt5rM7IdqsDhURgUVocRWH9z2Ejder9us53EqYI49rR1waKfyoj0Mq1ez1ugiAQrjy8BgmlVMnslNlMT5rORuNGDEpDcH+6fd3cu/ZeEsISSNAmBDLONYGc8/zIfPIj84FA5MyjGx+l3lzPqyWvctO4m/aZ2743fF4v3S1NxKSmD9v7s3R3UrVuNTkTp6KLDPxf8qbMIC4jm5TCkcMgbovoIiKxdnexffm3ACRk55FcUHSI6w0RIkSIECGOXkLCeYjfJAq1jLxJe3okh05MShhxmeEYa3rZ/kMzBdMSiIgPRADsfD5acDqdPPPMMzzwwAO0t+/KbZTJZPz+97/nztsWkWzfDmvuBVef4CHXQM58yDkR1BEg2b+Il+v1ovvPTzQ2NnLTqxs5+R/5ZO/RpqrdGixWeO2sLCAQU+H0+tk9qUWnknHVjEysThelm9s4c3TCoR6Gox63280///nP4N+33/UvviwLxAn9blzSL3qD7vV6eWvbl3xTt5ZaWxNOmQOVKvA5FwURARkRkkiKwjM4r3AeMzLG47NYcJbswL30C8QLLgjuv6BSgigiT0pCVViIqrAQZU52MAJkJx5jOx2PPobfbEGekkL0DTcSp9Py8YYNXHnhhaRv2ky2Uonn7Xf41+Yt3PjhB+j3yLBWJCcRde21dC5ejG5zCfdFTuFqVxMe0cHCr+7igzMfJl6/bzFydLKBSK2Cbpub9XXdTMs+MPHS7PSwtbGHjfUmdrSY8fl3dRhE6RSMS41gVFIY5evbOG1Saj+n2+FgWe1GBCDCGUZMVjaOTZsBUObn73UZzbhxSNRqOp95FldpGcZ//QtvRyeqMD2+nGzcbjdtbW0kJQVG37z66qvBZRcuXHjIn11frwvLiia8XYGcf1VuBNoJ8Qjygeel2tpa1q1bh8/nQ6PRMGPGDCIjI+ns7MRsNlNdXU1BweCdBIfCAw88QHPzNlLTNiGTe/n88/UsuvmhIYnmDQ0NXHLJJaxcuTI4bezYsdx2222cc845h/0zsScej4fGxkba2towGo1BoXsnUqmUmJgY4uLiiIuLIzIyElEUsdvtWK1WbDYbVqs1+LDZbLhcLtzuQLSFXC4nPz+fvLy8A+rEiI6O5qSTTmL16tW0trayZs2aYFb4nkVZjwYsFgurVq2iqyvQsZiTk8PYsWORyWSMHDmSlpaWoOtcrz/6RqcNN+HhY9Dp8mlr+4jOruWYTGuwWEvJyb4dhRAQ+ES/iKfNhuIAjAfK7Gzsa9fiqqrea5vfxUXQ5PSwwWzj8Xoj9+QkESkf+mcmTBHGVSOHXlz6WMXp8/NIXRulNidKicAt6fEU6AZGTAmCwDlxEfynz3V+Skw4EXs5nhJBQlH00SeqyiQyJiVM6jetx99Dp6OTblc3JZ0l/eYZlAbOyT2HsbFjkUlkLCxayL/X/Zua3hq+qP2C07NOP6Dtu+w2lr/yPC67lXlXXX/I+eDdLU1Url1FS2VZwJggCIw98TQAwmPj9ppRfqBEJiZz/MJrgsVD5UoVo0889VfZiRQiRIgQIULs5Oi70wgR4ijA7XYjk8mQ7EMwHjEtkY56C6Y2G+s+rWXyWVmExxx4cavDhdvt5uWXX+bee++lubk5OF0ikXDJJZdw963Xke4sgQ33BTLMATTRkH8KZB0P8gN7LzKZjOuvv56//e1viKLI4sWLefzxx/u1mZMfy8jkcPy7iYcyqQTdHu5ajULGlKwoPB4P9qpjo4jWofL8889TW1sLwLx58+jRZ+Fp6yAnLoyRv0BETZmxmlc2f86W7go6vJ34hF1FKvEJRKjjKYoewayEUcyKzUMtleOurcVZXILx7W9x19cHm+tmzUKeEOj8CD/jTAznnotsHzEL3o4OOh57DF9vL/LERGJuuhGprq9jKiKC97/8knv++U++X7yY43VhRJaX89ikyZz31psU7BHFocrNJerKK+n58COKZp3EnR0p3LXpGWyYWfDpP/jknP+iV+1dpJFIBObkx/LBxia+K21natb+M+a7bW421ZvY1GCiwmjZ3VxPXLiK49IiGJcaQVqUBkEQ8Hg8VB6hj3iDuwMEkYxeGZJEDYgisvi4ff4/AFQFBcTcfBOdTz6FtyNQWEx73HhSs7MpLy+nrq6OpKQk/H4/r7zyChA411x22WWHtL+ix0/Pl7WITh+CUopuaiLKQSKwfD4fmzZtorKv0GlCQgJTp04NOpkLCgpYu3YtZWVl5ObmDns2uMdj5C9/yWHFihrKK7p5+aUa1q65nK+++mqfwve7777LNddcE8z5lkgk3Hnnndxxxx1HXAy2Wq1UVFRQXV0djF/ZuU8xMTHExsYSHx9PZGTkoMcvLCxsr7EjHo8Hq9WK0+kkKirqoF3/KpWK2bNns337doqLi6murqa7u5vp06cfVZEn9fX1rFu3Do/HE8wyT0nZFeERFRVFUlISzc3NFBcXD8uojGMBqVRFUtICIiIm09DwIi6Xkdq6J8nO+hsSFJi/b8DTbCVsTsqg3/PBUOYEuujddXWIHg/CIN83QRC4JiUGY7WHRqebx+uM/CMrAcUQTAG/Few+Pw/XtlFpd6KSSPhLRjy5+3DmF+nU5GhUVNqdfN7RyyUHkR9/tJEsS+akMSfR4eqgzdYWfJjdZnpcPSgku85bapmaGUkz+Lb+W76p+4bciFzyIvOGvC2lRktYVBQOSy9lP//I+FPPOuD9FUURY00VlWt/prNx1zVXfHYuqYWjD3h9Q2Vn8dDMcRMRBFCof921h0KECBEiRIiQcB7iN4Xf52frd00k5RmISQ0bVAB7/vnnufrqqznjjDP48MMP9yqwaPQKMsfGULXBiFIrR6E+OrL9vF4vb775JnfffXdQiN3J+eefz/1/XkiWewcUPwb0KXoR6VBwBqRMGhCvMmRsXdwwxsN7KWo2Njp46aWXuOeeewjfwwV8uAosHsvYbDbuvffe4N+33Hkv71cGxMlzxx8Zt7nb62F1VzXLW7axtbOEWuP2XXEqAkiQESmJoigiiwsL5zM5bddNmW3VKlre/wD/bkVmAeQpKagKRyCodt18y+Ni97kf3q4u2h99DJ/JhCwhnphFNyPdo3iVRCLhrnvv5dOJE/nv76/kLIWcaKuVpWeeRdX/3cnpf/hDv/bqMWNQFRUhyGScERtLu83EU+VvYfJ3cMFHt/PpeY/uU6ScnhPNJ1uaqe+yUdNpIytmoNBuNDvZWG9iU72J2k5bv3mpURrG94nliYZfrnOt1+3ArXAgeATmdAiIUYHIDVXBiCEtr8zIIPbWW+h4YjG+nh6002eQpg+jvLycpqYmPB4PK1asoL6v0+SEE04IutAPFkEuQTMmFne9Gd30JKTagaKYzWbjp59+Cjp7i4qKKCoq6tfxmZ6eztatW3E4HDQ0NJCRkXFI+7U7Tmcr1dX/RRCcTJ12Jv/972s4HCLff/89N9xwA08//fSA77DVauWmm27ipZdeCk5LS0vjjTfeYPr06cO2b/tDFEWMRiPl5eX9OljDwsJITU0lLi6O6OjoQxbx5XL5sGWSC4LAyJEjiY6OZtWqVZhMJr766isiIyNRKBTI5fK9Pu98rVAo9ts5fjB4vV42btxIdXXA/RwdHc3UqVPRDVKAb+TIkTQ3N1NfX09RUdGA38pfM4Hs/0VUVv4Lp6OJhsYXSE/7I9JwBZ5msK5sQqrLQBa1//OlLC4OiU6H32rF3dCAMitr0HYqqYRF6XH8X2UzNQ4XLzR1cl1KzG+iY35/2Lw+/lPXRo3dhUYq4baMeLI0+46zCWSdG3iwto3lfVnne3OdHysoBAXZhmwK5P1HJdk9dtpsbcRrAyNVOx2dPLbxMRxeByOjR1LcWcxrO17jbxP/Rphi6B14I2YeT3tdDQ0l28iZNBV99L6vj3ZHFEV+WvJqUDCXSKUkF4zsW8+RiTZUakKCeYgQIUKE+G1wbF/hhAhxgLTV9NJSaaKzycLxlxUglQ0UM2677TYAli5dyksvvcQf9hDhdid3YhxRSToMsWpkil9WOPf7/bz33nv885//pLy8vN+8M844nYdvXkCOtwyqdxXtI2EMFJwOcYVwqDePlV+j6Srm3vNHc8p/12C1Wnn55Ze5+eab8fj8dNvcxOkPLE/+t8ITTzyB0WgE4JxzzqFOjEEUuxmVbCA79vC5KMuM1by0+VO2dFXS5e9CpVMDgc+BRCpF4VWTqopndvI4Lhl7KmFyNe6aGhybS3CL4SjS0wNtw/T47XYkGg2qEQWBCJaCAqQGwwHtj9dkov3RR/F1dyOLiyP2ppuQ7sNFevrpp1OwehVXn3UWszq7iJHJqL3rbp5Ys4brn3uuX6eXsJvwd7Emj3bFBN51r6PV28xFH/2Dd897cK/b0avkTMyIYlVVJ8vL2smK0SGKIk0mB5saAmJ5k2lXnIUgQFaMLiCWp0UcNZ1FX9Rtwuf3ovSrma6Lw11bB4Aqf+guOXlCAvF33oG324QiOQmFKKLT6bBarTQ3Nw9LUVBPmw2kAvKYwE25Ki8CVW4EgmTgOaq1tZWff/4Zt9uNQqFgypQpg4r1UqmU3NzcYN50enr6sAhmTlcb1TUP4/WaUamTKcy8hSVLzmbOnDm43W6effZZCgsLueGGG4LLbNiwgYsuuijojge48MILefrppzEc4HfmYPF6vdTV1VFRURF0uwPEx8eTl5dHYuLRn9+ckJDASSedxM8//0xnZ2fwHHog7C6m7/l6p8A+2PSdz7ufY0wmEz///HMw472wsJCRI0fuVZyPjIwkJSWFxsZGiouLj2iHydGAQhFFevr1VFc/hLl3C21tHxN/3Fn4et14mq2Yv2/EcGoGEs2+o4oEQUCZnYVjy1ZcVdV7Fc4BYhRybkiL48HaVlb3WElVKTgt1jDM7+zowyeK2Hx+bD5f37Mfe9+zzedjXY+NBqcbnVTCXzMTSFcP7TercDfX+WcdPVyaeOTrMBwJNHINmYZd9RQilBFEqiOp6amh29lNrCaWdns7b+x4g2tHXzvkc2dEQhKJuQW0VJRSuvIHJp19/j7be91upHI5giAEiu+mpNHT1kr62PFkjZ+ERv/b6XwLESJEiBAhjiQh4TzEbwZRFKnZEnDxpo+MRjpIkb9XXnmFyGgN95ycxYpeP3//+98599xz9+qUEwSB6ORftgioKIosXbqUO++8k+Li4n7zTjlxHo/ccBZ5/kpo/TgwUSKDtGlQcBoYUodnJzxOqFoGQP6Zf4b/Bi7+Fy9ezA033MC6WhMv/1zLzNwYLpuSPjzb/JXQ3d3Ngw8GRFuJRMJ1f/k/3irtRhAC2eaHi8U/L+Gl6g8RhV2FCBG15EePZELcSE6ILyIzPA6vyYRzxw6cr75FS3kZfnufQCwSFM5VebnE/uVWFOnpCAcZgeE1meh45FF8nV3IYmKIufmmIQnv2dnZfLp2LX/8/e8xfvcdRSo1fP0N/5o2nes+/oiY+P61DFw1tXQ+/TSXCtA5YyTfu7dR4azk1i8f4eGT/7zX7RyfH8uqqk7W1XYTppKxpbGHdvOuIokSiUB+fBjj0iIYm2LAoBneApTDwY9Nm0GEBJcBXWoq9jVrQSJBmZt7QOuRaDQo+pxmgiCQnp7O9u3bKS8v54MPPgACkTpnnHEGJtMabLZq4uJOQy7f90296BOxb23HUdyJRCvHcEYWEoU0IELsoUOIokhJSQnbtm0Lbm/GjBmDOnt3kpOTQ0lJCT09PbS1tZGQcGi1E5yuNqqrH8brMaNSJ5GV+WdkMh1Tp07lxRdf5NJLLwXg5ptvJjs7mxNOOIH//ve//OMf/8Dr9QKg0+l46qmnuPTSS4+IUO31etm2bRu1tbXBzHGpVEpmZia5ubnHnOtZq9Uyd+5cOjo6cDqduN1uPB7Pfp93FmD1eDyBSLA9RssMFalUGhTWbTYbfr8ftVrNlClTiI/ffx2VoqIiGhsbaWhowGQyDZsr/1hBq80iOeVyGhteor39C1SqBMJnTaD38xp8vW7MyxsJPzF9v+tRZmf3CeeVcOIJ+2w7QqfmkoQoXmvp4t22bpJVCsbofx3OWYvXR5nNSZnNQZXNRY/Xh83nw7VbPN7e0Muk/DUjntQhiuawh+u8y8JpMYZj3nU+FKQSKQsLF/LA2gdotjYzLnYc3c5uSrpKWN64nONTjx/yugpmzKGlsoyWilJMrc1EJAy87nPZbdRsXEf1pvVMOP13xGUG4olyJkwhZ8IU5KqQKSVEiBAhQoQ4nPz6r25ChOjD1Gqnt92ORCohtWhgFqPP5+Oxxx7j5unppLT1cJ5CYLnfyV133TUgq/toQBRFvvnmG+644w42bNjQb94Js6fy2B9PoUBSB70rAhN3FvzMPQk0kfvfgMMExh3gc4PfB34vRGZATJ871WmGss8C+ejWdnDbQBdLxrRzOOGEE/jmm2+oqanh008/Y4skG1EMxbQMxn/+8x96e3sBuPzyy9lmVgEuJmZEkhJ5eG7m/7f6HV6q+QBR8KNES4YqmeNTx3HR6FPQ9RX99PX20nbvfXh2i28AkGi1qEaMQJm9q/SroFDs0+W3P3y9vXQ89jjejg6kUZHELLp5v5nbu6PVannl7bd5/LHH+OLuuzlZpyO6qYnFs2fzp+XLid1NIFWkp6EePRrHpk38eXUvxolZ7KCOja5q/H7/Xt2hGdFaMqK11Hba+KYk4GyVSQWKEsMZnxbBqBQDOuXR/ZO6pWUTfsHP6IyxKKQB4VyRkY5EfWjxMWlpaWzfvp3Nmzfj9/sBuPjii5HJvDQ2vYro99Jr3kxa2jXotDmDrsNndgcKgHYGOmbkCdq9Cskul4vVq1fT0tICQFZW1pAKRCqVSrKysqioqKCsrOyAhXNRFHG5WrFYdmC1lmK1luP3u1CpksjKvAWZbNfoiEsuuYTS0lLuv/9+tFotL730Eu+//z5LliwJiuYTJkzgrbfeIjt7zzLKw4soinR0dFBaWkpDQwMOhwOJRIJWqyU3N5esrKyDzhw/GpBKpUMSqXfH5/MNSWDf+bzz9e5/71yPz+fD6QwUrk1MTGTy5MmohihkRUREkJqaSkNDA9u3b2fGjBkH9uZ/BURGTMHlbKG9/Ssam15FkRmLfm4qPZ/X4u1wYF3VgnLyviMsdv4euatrEEVxv51Q86L0NDrdLO+2sLjByM1pcYwMO/bEc7PXR5nVQanNSZnNSZPTvc/2KokErXT3hxStTEKYVMqcyDBilQdeiLhQpyZXq6LC9ut2ne9JhCqCi0dczPPbnmdT+yYmJ0xmTesaPqn6hGxDNqn6oRlT9NExpBaOpmH7Fnas+J5pF1wanGfrMVG5bjUNxVvweQPnnMYdxUHhPCSYhwgRIkSIEEeGo/suP0SIYaRmawcASXkRKNUDP/pLly4lQiuQ1NEbKHbjFrn5lALueuop/vCHP1BUVHSkd3mvrFixgjvuuIOVK1f2m37S9HE88oc55CvbEDwBJ+YBF/zsbYLSz6BuZUAs350RZ+4Szj122PFJ//m5J4NEwk033cQ333wDwMPPv0neeX9BIZMwM/fI5C4eK7S2tvLEE08AoFAouOyGv/H61l4kEoGzxhwet/mza97j+cr3EQU/EUI0H/7uYcJcPpzbSxDXrIfZswGQ6PX4zGYQBBRpaYH4lcJCFOlpCMOYC+yzWGh/7DG8RiPSyEhi//xnZJFD6NjZA0EQuHnRIsaNH88dCxZwDhBvtbH4pJO57aeVwcKBgkRC1MIr6LDZcJWXc+c2HddOjsQtOljatJmzUsfvdRvnjE/m9TX1pEVqGJcWwcikcFTyo6O2wf7Y3LQDh2hBEOHE5JG4ftoMgCq/YD9L7p/w8HAiIiL46aefSE1NpaqqioULF2IyrULsO4d4Pb1UVz9MYsJ5REfPDQpboijiqu7BtrYN0eNHUEgCBUDTB3c+d3d3s3LlSmw2GxKJhAkTJpB1AJ02eXl5VFRU0NraOiSHr9vdhdVahsVaitVaitdj7jdfpU4hK3NRP9F8J/feey/l5eW4XC5kMhl2u5358+fz448/cv3113PXXXfts3DooeLz+aivr6e8vByTyYTf70cURWJjYykoKCApKWnYM76PFaRSKVKpdMgC956IojhAYJdKpURF7b948J6MHDmShoYGGhsb6e7uJvIgzn/HOvHxv8PpbMVs3kpd3VPk5PyDsNkpmL+pw91gRj7CsM/l5cnJCAoFfrsdT3MLiuR9/34KgsBlidF0ebxsszh4pM7IH1NjmRCuHcZ3Nfz0eryU2ZyU2pyUWh20uDwD2iSpFORrVeRrVcQoZAFxXCpBI5UgPQyjWgKu8wj+XdPK8i4Lp8YYiPwNuM4BRseMZmbyTFY0raC4s5i8iDzKTeW8tP0l/jbxb6hkQzu/FEyfRVNpMRKpDK/Hg7Wrk4q1P9NcviNYayYiIZGcidNIzM0/nG8pRIgQIUKECDEIv40rmxC/aURRpPiHZoy1AcEjY/TgbphHHnmEiyelI21swSOTIPP6KeixM60ohRtvvJHvvvvuF898XbNmDXfeeSfLli3rN/30qQX8+/JpFOjMCDSCj4Mr+Gnrgs9vpV/RUHUESKSBiJfw5F1tFVrIOyUwXSIDlR6y5wNw0kknkZubS0VFBdtNEhLMZs6YmH3UO3KPNPfeey8OR8Bhe+1117GqNRAfMDM3htjDkAf/fWspz9UE4lnCieQVzTycDz2KtbUNAGm4Ht2sWcH8zOhrr0EWG7vPnPGDxWex4CzZgeWbr/G2tiE1GIi5+SZkUQNHgxwIM2fO5LWff+b3M2Zwjl8koauL/5x0EncuXx501QpyOdHXXE37o4+hbWykqF3D5mgT71Uv26dwXpCg5/6zRx7S/v1SvL75SwCUop4JOWMwvvA2AKoRhy6cA8hkMkwmE2lpaWg0GsaMGUN5xZ0AJCaej91eS0/Pelpa3sFuryY5+Qokohzrzy24agMjLuTxmkABUN3g7ufq6mo2bNiAz+dDq9UyY8aMAxYZdxa9bGhooKysjClTpvSb7/M5sFh3YLWUYbHuwO1q7zdfkMjRarMJ041Ap8tHrU5FEAYXn0VR5Morr+STTz6htbUVn89HbGws999/P+edd95hE83tdjtVVVVUVlbicgXihKRSKenp6SgUCubMmXNYBfvfAoIgBDPQD5Xw8HDS0tKor6+nuLiYWbNmDcMeHlsIgkBq6lVUVf8bp6OZ2ronyc76K7rpScgMSkT9vq8dBJkMRWYGrrJy3NVV+xXOAWQSgUVp8Tzd2M66XhuL641clRzDzMjDV1fkQOlyeymzOSjvc5S3DiKUp/QJ5QU6NXlaFXrZke/MHaFVkadVUW5z8ll7D5cl/TZc5wBnZ59NVU8VLdYW1DI1BqWBTkcn75S/w+WFlw9pHZpwA/P/8Cc04QZEUWTjF59g7giMbIvLzCZn4lSiU4enLscvRbezG7vHTowmBqX04EefOr1OGi2NNFoaaTA30GRtQiVVkapPJU2fRpo+jVhNLJK9/C6HCBEiRIgQB0NIxQrxq8Dt8NLb6cDc4aC30wEijDsxDQjckPW02UAUicvQExY5UJBct24dlSVbSYpMB6AxfySRdRUYrA4umxDPVS8s5/333+e88847km8ryJYtW7jzzjv57LPP+k0/b0oGdy+YQH6kiCD0OSEPpOCn3wddVbtc5NooSBoPEgnkn7Zr+mAow2D84DcFEomEG2+8kZv+eieKhFxqa2uZd/nUIb7b3wbV1dU8//zzQCDn+KyFN/LG5i7kUgmnjzq07OXB+NFYxt3rH0epVqHxRvFsZwGSn5fjhYCrPDMD1YgR4PFAnxh0KPEreyL6/bjr6nCWlODcXoK7oSHopJKGhxOz6Gbksfsejj9U0tPTeeLLL7l57lx+J1eQUN/Av886mzs++zTosJVoNMT86XqMDz3EORU9bNA6KXVt4rvKNczNmTws+3E0sb2nHgFItCixf78cv82GoFKhSEsblvV/++23AMTExHDKKadgs5XhdrUjkaqIjJxJdPQ8NNosWlrepadnAw5nM+lp1yF6/SCAZmws6qLoQQuAer1eNm7cSHV1NRCIw5gyZQpK5cHdfOfn59PQ0EB9fT2jR49G05fX7vPZqai4F7e7c1djQUCjTkenK0AXVoBWk4lEsn+xVBRF1q9fj8lkYurUqSxfvhy9Xs/xxx+PzWbj+++/Z9q0aSQnJ+93XUOls7OT8vJyGhoaEPu+WxqNhpycHLKyspBKpXR0dAzb9kIMH0VFRdTX19Pc3ExXVxdRh9iBeCwilarISP8TlZX343Q00tj4EmmZgUKLO6Nx9oUyOwdXWTmuqip0Q+x8kEkErk+NRd3cyY/dFp5v6sDh93Ni9JHP+hdFkXZ3wFFebgvEr3S6vQPapaoU5OtUFGgDQnnYLyCU78lO1/kDNa0s77ZwWuxvx3Uul8r5fdHv+brua87LPY82WxuPbnqU9W3ryY/MZ1LCpCGtRxNuAALHMm/ydNqqK8iZNJXw2AOLoToaabO18eC6B/H4A9/jcGU40epoYjWxRKujiVHHEKOJIVYT209U310kb7Q0Um+up8PegcjAzP46c13wtUKqICUshTR9GqlhqaTqU4lRxxzTHQ8hQoQIEeKX5bdxVRPiV0ndtk46myz0djhwWvvfVElkEvx+EUmfCJMzMR5BgOiUwQvHPfLII9x43nQUDfU4VTLOu/khXn/2v4Sv/4bIFgsLTyjglltu4dRTTw2KLEeC0tJS/vnPf/Lee+8Fp8klsGBKEn8/exQ58TokAiCRH1jBT48DqpdD+edg74YzngyI5gAz/hxwmB8il19+OXe/sQwEgbrNK5C5zwQOLUv518Q///nPYNbxokV/ZnldoDjdvBFxw15Y8qOyFTxWsQSvz02yPouH6mKRbN0MMikRF1yAeuw4pLrhH6Lu6+0NFBYtKcG5oxT/HgX45CkpqEaMQDdr5kHFs+yLoqIi7vrgAx4480xOVWuI3raNh6+4gr+8+mrw5kkaHk7U768k+6GH0NlUdKrNPLXp/V+dcO71eumiCxAZ0wGesMAIA2VuDsJ+csGHuv7XX3+dESNGEB8fz9SpU+nq+hGAiIgpSPtuhGOi56JWplJf/ywuZyuVVfeTPPJSwkcVIY8Z/LxqtVr56aef6O7uBmDUqFEUFhYe0g1wdHQ0MTExdHR0UFFRwZgxYwDo7Pwet7sTmSwMg2FCQCzX5SKVHvg5v6ysjJqaGgDmzZvHZZddBgSKUf7888+0tLSwYsUKJk6ceEgZ5z6fj8bGRsrLy+nq6gpOj4mJITc3l+TkZKR9BXuHIj6G+GUIDw8nPT2duro6iouLmd0XmfVbQ6GIJi39Omqq/0tv7yaMxqXEx5+J6BdROCX4bR4wDD5aQpkT+B65qqqHlHO+E4kgcGVSNBqJhC87e3mjpQu7z89ZsYbDKrSJokizy9MnlAcKevZ4fP3aSARIVynJ0wWiV3I1KnRHgVA+GAV98TBlNieftvdw+W/IdR6vjQ+6yzMNmZyacSqf1XzGO+XvkK5PJ04bd0DrSx5RRPKIoyce8lDw+X28tuM1PH4PMokMr99Lr6uXXlcv1T3VA9rrFXqi1dHYPDba7e2DiuQGpSEojCeHJePwOmgwN1BvqafJ0oTb56a6p7rf+tUyNSlhKUFnempYKpGqyJCYHiJEiBAhhkRIOA9x1CKKIvZeN70dDsydDqw9LsaflBa8yOlstgbjVwA04UrCY9Too9WEx/QXaBOy9u4eqq+v5/333+eMswMXqcakZJLj47jx/JN4o3It8T09zElS89Z3pTz44IPcfffdB/V+duzYwfPPP4/FYkEmkyGVSpHJZP0eu0/bsWMHS5YsCRbb0yng0klxLDo1n6ykmIBz9kALftq7oeIrqPw2kFEOoNSDuWmXcD4MojmARqNlxIxT2FHThLn0Z5599lnuuOOOYVn3sc7WrVt56623AIiKimLmuVeyZJMRtULKyUXD6y56e8uX/Kf4ZZBBVsxI/pd/Oa7PH8MvkxJ99dWoR40atm2JXi+umpo+sXwHnsbGfvMlGjXKggLURUWoCgqQGgzDtu3BmD59Ote89BJvLFzIbI0WzXff8+xtt3HtQw8F2ygzM0j4132cuH0pb9Yvpc7dQKu5nQT98LjfjwY+L1+JFzcSUcIpFiW+roCjejjyzQG++uor2tra0Gq1jB49mu7uRtSaLQBERe5yfvosbrwrpSTqrqQr6TNs1goaWp8nJuZEEsSzEYT+556WlhZWrVqF2+1GoVAwbdq0Ay7ouTcKCgro6OigsrKSwsJCJBIvHR0B13xi4oVEREw86HU3NTWxeXMgQ378+PEkJiYG58nlcmbMmMH69eupqalh3bp1OJ3OA+4McDqdwTiWnXFPEomEtLQ0cnNzf5OO5WOdna7zlpYWOjs7iY7+7QiPu6PT5pCcfCmNja9gNH6GUpmAsCWOlDoN7lozyrGDd2Qp0tNBKsVnMuHr7j6g2C9BEFiQEIlGKuEDo4kPjSbsPj8XJQyfsOYXRRqdbkqtAaG83O7E4u0vlMsEgUyNMphRnq1RoZYeG5ETO13n99e08kO3hdNiDEQpfnu3maIoEq4MJ12fTp25jpdLXuaW425BLvltxmN91/AdDeYG1DI1/5j0DxRSBR2ODjrsHXQ4Omi3twdf2zw2zG4zZveue7udInmqPuAcTw1LJUwxME5pQvwEAPyiH6PNSL2lngZzQzDOxeF1UGGqoMJUEVxGI9cERfSdz+HK8JCYHiJEiBAhBvDbu6IJcVTT0WChvd6MudNBb4cT3x7uG4fFg0YfcOMm50cQlahFH61GH6NGrjg4wfeJJ57gz5fOR9vWgE8qMCNbDZ9cj9bZgzs9E/+WLYS1ufjrOaN48MEHueKKK8jIyDigbSxfvpwzzjgDq9V6wPsXpxW4bGIkV8/NISM1KeAe1ERB/qlDL/hp74Zt70DdT7sKfoYlBOJYMmaCbHgdzgASicBz18xlzNzf4Wmv4amnnuK2224blkzYYxlRFPnTn/4UjFL4699u59uKHgBOGZmAdhhz4N/d+g3/KX4FP160XgOLJ15HtD4a96Kb8XV3D4to7jWZcJb0ucrLShEdzn7zFWlpqIoKUY0YgSI9HUF6ZN1yZ5x5Jp0PPcTKv9zGBI0G51tvsSQujgW33hpsI4uMZNHUS3iv/lvcOHhgxcs8cdpfj+h+Hk6Wlq5EAMLcetIyc3CXlQLDl2/+8ssvA9DY2EhqaioO50Y8bhcGQwFqdSBr2Fndg21Na6AAaI+E9LF/ol39GR0d39DR8TV2Rx1pqVcjl+sDdSmKi9m+fTsAkZGRzJgxA612+EZFJCUlERYWhsViobq6moiIanw+O0pVPAbDcQe93u7ublatWgVAdnY2ubm5A9pIpVImTZqEWq2mpKSEbdu24XA4GD9+/H6LdXZ3d1NeXk59fX2wU1WlUpGTk0N2djZqdWhUz7GKXq8nIyODmpoaiouLmTNnzpCX9Xq9lJWV0dPTQ2xsLElJScP6fTnSREZOw+lsoaPjGxqbXiHesBAAb7t9r8tIlEoUKSm46+pwVVYdcL0MQRA4Ky4CjVTC6y1dfNXZi93n5/fJ0QdVVNPrF6lzuijrE8or7E7sPn+/NgqJQI4mIJLnaVVkaZQojuGCvQU69S7XeUcPVxxm17nXL2LyejF5fHR7vHR7dr3u6nvtFUWmGXScGB1+RIT8j6s+5ruG78iPzEcr19JkaeKTqk84N/fcw77to40Wawuf13wOwDm552BxW9Ar9UGhek/sHntQVFfL1KToU9Ar9P3amN1mbB4bWnng/LalfQuf13zez0mepEsiQZfA5ITA6EGv30urrZVGc2NQUG+2NmP32CntKqW0qzS4fr1CT4o+hbSwQF56qn5woT5EiBAhQvy2CAnnIY44Po8fc5cDc6eT3g4H+VPiUagCH8WORgt123bly0pkEsKiVIT3ieMyxa4biviMQ8+gNJvNPP/88zx2WiDL25gQze9ipTDuUmhaz59YzRNNsaR3tpGnggi5m1tuuYUPP/xwyNv4+OOPufDCC3G5XORGSVDJwOcHnwj+vofPL+IT+0+PVAssGKvnohlZpKenIZPK+gp+ng4pk4de8BNApoSG1QHRPCY/sI6k8fvPQD9EcrMyOXl8Fh/Wb6WtrY333nuPiy+++LBu82jn9ddf56effgICwtqoEy/k3Y0thKvlzC0YPpfzB8Xf8u9tL+LHgxY9r09aRLw+cBOrSEmBlJSDWq/o9eKqrsa5vQRnSQmelpZ+8yVaLaoRI1AVFqIqHLHfwqJerwWpVHdYHT6/v/JKOoxGSh95lAKVipZHH+PLuDhOvvTSYBuZTMYYdTbr7MWsNW3H6/UiG4YYk6OBem/gnJpsUSCL0ePyeJEaDMjidg0f//e//83y5cu56667BhTM3BednZ18+umnQEDgHjVqJHb7T9gdUrKyZuN3+7CtbcVVHSgAKovVEDYzUAA0Mew8NJoMGhtfwWYtp6LyHhITrmTLljZaW1sByMnJYdy4ccG4keFCEATy8/NZv3495eXFZGatBCAu9vS9FvvcH3a7nRUrVuD1eomPj2f8+PF7/VwLgsDo0aNRqVRs3LiRyspKnE4nU6ZMGfC58/v9NDU1UV5e3i+jPCoqiry8PFJSUob9+IT4ZSgsLKS2tpbW1lY6OjqIiYnZZ3tRFGlubmbjxo3YbDYAGhoa2LBhAxERESQnJ5OUlERERMQx56JMSDgHp6sNi3kbbf43QHo83g4Hol8ctBYCBOJa3HV1uKqr0E4eWrb0npwQHY5GKuG5xg5WmCw4/H6uS4lFvpdt7sTt91NtdwXc5H1CudvfP2ZCJZGQq1WSrw0IzBlqJbL9rPdYY6fr/MduC6cfguvc7fcPEMH3FMfNXt8gQR4D+bKzl2+7zEw16DglJpwk1eEzcIyLG8fyxuWUdZcxKWESa1vX8kPjD4yJGUN2xMHHch1r+Pw+3ih9A5/oozC6kIlxE7l1xa24fW4UUgWxmthgtnmMOoZkXXJAsJanBUV1s9tMSWcJDZaAc7zB0kCvq5ezc85mbupcIBCT02prpdXWytrWtQBIBAmJukTS9GlMTphMRngGKWEppISlMJVAvSWPz0OLrYV6c31w3a3W1uA2SzpLgu/FoDSQpk8jRZ9Cuj6dlLCUoHAfIkSIECF+G/w6VIEQRzVup5eWyh56jHZ6OxxYTa5gUUCAxBwD0cmB7PHYtDAQQR+tQh+jRhehCuaUHw5eeOEFRhUlEdltRxQgMysy4OZOmQwx+QjNG9FlJOHt7EDb7uXO80Zx/csf8e233zJ//vz9rv+VV17hyiuvRIKfv05T8LsJieTn5SMCouhHFANFE0VR7PfwiyISQSAiMhK5THZgBT99XmhcA23FMOnaQHuFFo67EvSJEJ0zLMduX1icHnRKGYIgcPPNNwc7Gh577DEuuuiiY+4Gfrjo6enhL3/5S/DvRx57gq92BISw00cnohym7NKlJcu5f0tANNeg5xnPZBRPvYjrhj+hPIg8ZW9XV8BRXlKCs6wc0eXaNVMQUKSnB4VyRVoawn4cc253Fz29G+jpWY/DXo9eP4r09OsPWrAcCrfdfju3trZS/867pCkUbLv9dsJjYph60kkA2Nat4w8/tLLxOAGX1MZz6z/gj1MuOGz7c6Rwej1YBDMSYHaXHDEiMOJEVZAf/B6Wl5dz++23A7BixQree+89TjvttCGt/8033wxmZ1966aUkJXmorrZjt2vR68di+bYBT5s9UAB0TAzqkTH9RC+D4ThUqiTq6p/GYqln3bq/YbGOQSotYOLESQc8uudAyMjIYNu2bXh9G7Bau4iIyDxot7nX62XlypXY7XbCwsKYPn36kMTsvLw8VCoVq1evprGxEZfLxcyZM1EoFLhcrmAci72vPoAgCKSmppKXl/ebjfL4NRMWFkZmZibV1dUUFxdz/PHH77WtxWJhw4YNwU4mjUZDRkYGRqORzs5OTCYTJpOJ4uJiNBoNSUlJJCcnExsbe0x0tAiChLTUq6is+jcOexOOxC/xu/6Iz+REFjX4yApldjaWb5fhKq84oJzzPZkeEYZKIuGphnbW99pw+Nq4KS0O1W6xKU6fn0q7M5hRXm134RX7y7g6qYQ8rSoglOtUpKoUSH7l1z8FOjUFWhWl+3CdO3z+PgHcS/dugni3x9c3zYt1D3f+3pAJApFyKZFyGRFy2YDXZq+fLzp6KLM5WWGysMJkYbxew2mxBrI1quF++6Tp0zgz60w+qvqITcZNFEYVUtJVwtLqpSwav+g3c/37bcO3wYiWi/IvwulzopPrMPlMuH1umixNNFmagu2Loou4dvS1ABhtRp7Y/AS9rt4B6xUQ+k2PVkdz9airg+J6vbkem8cWXH+OIYeM8MB1RIO5gdUtq4OxL8m65H7O95371WBpCArq7fZ2elw99HT0sLVja7/t7sxZTwlLIcuQhUwSklVChAgR4tdK6Awf4rBTv72LirVt/aYp1DLCYwJZ5Crtrty/6OQwopOHZ0icKIo4nc1YrWVYrWU4HA1Ex8wlNuZEICB0PP7449w8PRa67HQnhPH7VDnknRxwc2ujofBsrvS9y78bU8hvqyPc4WVcgoQbb7yRbdu2IZfvPbPwkUce4ZZbbkEphX/MVHLa+FRGjx2PJDwJRD+IPvD7+l77d3vdN12QQMqkoRf8dNuh+nso/wLsfUXiMmdDbF8cQ+asvS463Dy1vBqry8OV0zOZPn06Y8eOZfPmzWzYsIHVq1czderUI7YvRxN33nkn7e3tAPzud79DSBmNeVMzMWFKZuQMjwj2WemP3L3pWXy4URPG0/4pGNZtQRQEfCbTkNYhut24qqpw9Inl3jZjv/mSsDBUhSNQFxaiLBgxpMKiHo+Z3t4NmHrWYbf1LwhlNm+jpfU9khIPn1AtCAIPPf44V3Z0oP7hB2JlcpZffQ36D96naMIElDk5xPvlpJu0VEdZ+bDmh1+FcP5jSykenxOpqOB4UYunuRkAZcGumJavv/46+NrpdHLWWWfx0ksvBQta7g1RFHnppZeCfy9cuBCp9EsEiYDdnkZHbQeyNjtIBMJPSkceO3g2sVIZj8D5tLT8D7msg7CwTaSlRpOamjho++FCJpORk5NGQ+PrWK0iebmnHlTnjSiKrFmzhq6uLhQKBbNnzz6gSKq0tDSUSiUrV66kvb2db7/9lujoaOrq6vD5AnFlSqWS7OxscnJyjmiB6hBHnp2u87a2NoxGI3Fx/QsLer1eSkpKKC0txe/3I5FIyM/Pp7CwMHhN4nQ6aWlpoampidbWVux2O5WVlVRWViKTyUhMTCQlJYXU1NSjWsiTStVkZNxAaek/cGvb8HpNeIz2vQrniqxsBLkcb3s75qVLCT/zzIPe9nHhWm5Jj+PReiPbrQ7+U9vGaTHhlPeJ5XUOF3sYygmXSQP55H2RJUlK+VF9fA8UURTxNDUhT0jYZ2Hps+MiKO1znWulEnq9PrrcfW5xrw/HEEVxhUQgai+C+M7pYVLJfo/xWL2GKruTz9p72Gi2Bx/5WhWnxRoYpRveiKvjU4+n3FTOjq4dtNhakApSanprKOkqoSj611H0c1+0WFv4suZLAH6X8zvClYERwvdMuweP30OXoyuYa95h76Dd0R4UtwEiVZGY3WYEBGI1saTqUwM552GpJIclo5Lt6vCQSWSMihnFqJhA9KAoiphcpqCLPNOQGWxbaapkZfNKCFwGoZAqSNYlB7PTC6MLyTRk9lvG4XUExPS+9dWb6+l0dAYfm9sD9Uyi1FGcnnk64+P2PtIsRIgQIUIcu4SE8xAHjej3BoTe3fC4fDRXmNBFKIMCeEpBJG01vcRnhKPvE8uVGtmwX1iIoojL1YrVWh4Qy23l+Ly2fm2MbZ8SFTkLqVTFBx98QFKMhnRTwA0Zl5cUiDTJ2s3hlX86VC9nzgQ5dZ+3oO1xs3BeBje/UcbixYv585//POh+3HHHHdx///3oFPDPWUpOOi6LwtHjEWbeCgnDV4wRCOSXl38BVcvAEygSh1IPeSdBePLwbmsI1HXaqDRakEgEIjTyoOv88ssvBwKu89+icL5p0yb+97//AQFn4L8efJgn1wU6lM4ck4TsEAuAef0+Xqj8gddK3gqK5v/zTyVy9RYQBCIvvxzNhAl7Xd7T3t7nKt+Bq7wcsc9FDIBEgjIzo89VXog8JWVI31+v10pv72Z6etZhtZXvGmkiCGi1ORgMExAEGU2Nr9LZsQyVKomoyOmHdBz2hUQi4dnXXmPBKacwvmQHEcD7F1yA7uuvSc/JQT12DOeVr+HBSDBJTFSZjWTr4/a73qOZL6p+BgRihViixo7Duuw7AFT5+cE23377bb9lfD4fl19+OZ2dnYOe43ayefNmtm3bBsCkSZPIzIymrKwUlUpFb08OdbW15OpikUWr9yqae71e1q9fT21tLTCNhMRcDIYSXK5tVFU9QHr6dSiVh+9/EBnZTHOzB6czDLd7YObqUCguLqahoQGJRMKMGTMI20880WDEx8czb948fvjhB3p7e+ntDTjqIiIiyMvLIzU19VcTHRRi3+h0OjIzM6mqqqK4uDgonIuiSGNjI5s2bQqOQIiPj+e4445Dr++fA6xSqcjMzCQzMxOv14vRaKS5uZnm5mYcDgcNDQ00NDRQUVHBxIkTCQ8/9Oi7w4VSEY1anUa3tA2XvBGPMRP1iMHzy6U6LREXX0z3K69g/vIr5ImJ+/zd2x9FYRr+lpHAw3VtVNqdPFrfv4ZHtELWV8hTTZ5WRZxi+K9tjxZEr5fuV1/Fvn4DYfPnYTjnnL22LdCpGaFTs8PqYGl7z6BtNFJJP0E8su91hFxGhCwgjGuGIIoPlWyNipvT42l2uvmio5dVPVbKbE7KattIVSk4KVLH0OT8/SMIApeOuJQH1j6AyWlCJ9dh9VhZWr2UwqgDKwR9rOH1e3l9x+v4RB8pYSl8Vv0ZWrk2KGzLJXLitfHEa+P3ug65VM6tx91KnCaun0g+FARBIFIVSaQqkjGxY/rNywjPYG7q3KA73eVzUdNbQ01vDQC3Hncr2vCAEaS2t5YuRxdp+jSyDdnkROwarWvz2Gi0NAbF9EpTJV2OLl4peYXvG7/n7Oyz+7UPESJEiBDHPr/oXdiKFSt46KGH2LhxI62trXz00UecddZZwfmiKHL33Xfz3HPPYTKZmDRpEk899RSFhYXBNi6Xi1tvvZUlS5bgcDiYO3cu//vf/0hO3iUYmkwmbrzxRpYuXQrAGWecweLFizEYDME2DQ0NXH/99Xz//feo1WouuugiHn744d98IcPB8PnsdJQ9S0fDOyT4RCxtOiSKaTTs6KKlogef1090yi7nuEorZ8b5AwukHSqiKOJyG/sc5eXYrOV4vZZ+bSQSJVptNlpdHt3dP+F2tWMyrSYqajb//e9/uXhsIpKmNnrDVVyWIoOsuYFYk53IFDD+CibZHuLHzDRyq6qIcqo4PVfGXXfdxUUXXUR8/K6LP5/Px/XXX8+zzz5LpFrgnjlK5k/IJ6doHMLsvw1/TEpXNXxzZ8ClDoEolvzTIH3GYSn4ORSWlQbcyRPTIzFoAvtwwQUXcNttt2E0Gvnwww9pbGwk5SAzto9F/H4/119/fbCY35133sn2XjkOt4/kCDWTMyMPaf0vbf2Cj9p+otvehiCFqLBkHjIXEb16c0A0v+zSQfNeXZWV2Ddtxrl9O97dspMBpOHhAaG8qBBVfj6SIbpcfT4nvebN9PRswGLZ3q9zTaPJwGCYgMFwHHJ5RHC6x92N0fgpzU1voFTEoNPlHeSR2D8KhYJXP/6Y82bPZl5LK7FuDy+dfjo3/PADYbNmMW3jJlI9EXSG+Xm16nvuHbfgsO3LkWB900Z8gpd0XRLK5DSsgDwpCWmf0ObxePjhhx8AiI2N5YILLmDx4sUA3HLLLbS3t/PAAw8MeqO/sygoBNzmXd0/AhAZMYbWljCazUYmnD0FwTt4Cq3FYmHlypX09PQAMHr0GEaMGIHNVkF9w3M4nc1UVN5HasqVhIePGZ4Dshs+n5Oe3uVoNBraO4ooKysnPj7hgNZRV1cXLGI6YcKEAe7gAyEiIoL58+ezdu1alEolubm5xMTE/KpFlhCDU1hYSE1NDe3t7bS1taHRaNiwYQNtbYHOVo1Gw7hx40gZQiemTCYjKSmJpKQkRFGku7ubxsZGKioq6Ojo4Msvv6SgoIDCwsKjtnNGo87EJ12DU96E12jbZwyLdvIkPC0tWL75hu7XXkcWE4MiPf2gt52jVfH3zAQW17cjESBXu6uYZ4xi76MOf02IXi9dL76EY3PAXWv94UfCTjwRqU6312WuSIriQ2MPaolApFzW5xDf5RpXH6JZ4GBJUin4Q0oMv4uL4OvOXr7vNtPgdPNsUyc2VRQ1jR2kaFQkKuUkKRUkKOX9InqGSpgijMsLL+fJzU/S6+pFIkhosbawwbiBCfEH35lztLOsYRmNlkZUMhV2r51edy/r2tYFhfOhMljx0ENldze5KIq029uDLvJGSyNJuqRg27Wta/mpOVCPSC1TB13pO6NZ8iLyyI8MGBBcPhffN3zPt/WBeJrHNz1OYXQhZ2efvc8OghAhQoQIcezwi14h22w2Ro8ezcKFCzlnEOfCf/7zHx555BFeeeUVcnNzue+++5g/fz7l5eVBR9fNN9/Mp59+yttvv01UVBS33HILp512Ghs3bgxmOF500UU0NTXx1VdfAXD11Vdz6aWXBgua+Xw+Tj31VGJiYvjpp5/o6uri8ssvRxTFoIAQArxeG52d39HRuQy/swu/341TlLJl7YO4naPx2U4DbyS6CBWx6fpDypccDFEUcbs7Ao5yW0As93r6598JEjlaTRY6XT46XR5qdTqSvsw5iSCnpeUdurp/pLRURlNdFUnxgYuk3sR0QIC8UwZuOGk85J3CaQUpbPq/2wizOLhkXgE/vljM3//+92BUgdvt5rLLLuOdd94hXidw3/FKjp80ioyCsTDnH2AYBqFYFAMxLNq+WI+IjMBrTWQgAz1x3GEv+Lkveuxu1tV2AzBvxC4BSalUct1113HXXXfh8/l46qmn+Pe///1L7eYR5+WXX2bNmjVAINP48qv/xN2flwPwu3HJB/09+bz0Rx7e9BYmfwcqtRaVIoyT00/isgon3lXLAYi4+GK0gxR7tHy/nJ533901QSpFmZW1y1WelDjk/fL73ZjN2+jpWY/ZUozo3+VWV6mTMRgmYjBMQKkYPI4mLu50nK5Wens2UFf/DDk5/9hr2+FAp9Px2pdfcuH06ZxhtRFvtfHw2WfzwM8/I09K4oRWI2+GdfNz88/YRv4OrVx52PblQLFYLHR3dxMeHo5er0eyjzz5+u4W7ELgHDk/eTTO0jIgkG++kzVr1mC1WgGYN28ejz/+ODExMfzf//0fAA8++CCdnZ0888wz/UQ1l8vFW2+9FVifSsX5F5xDc/O/AEhNO5WqqlpcLhdGo5HExIGRK01NTaxevRqPx4NSqWTatGnBTkidLo/cnDupr38Wm62KurqniI09ifj4sxCE4ctm7uxajs9rIyIyg+bmdFpaWujt7R2y+7azszP4vS4oKCArK+uQ90mn0zF37txDXk+IYxutVkt2djYVFRWsWbMGp9MZjGU5FJFbEASioqKIiooiJyeHDRs20NzcTElJCfX19UycOLGfGeBoQaPJwi8R8cZ3oMva/7VU+Fln4mltwVm8nc5nniXub39FuptZ5kBJUyt5OP+309m/O6LbTefzz+Ms3g4yKdLwcHxd3Vh/+JHw007d63IJSgXXpw5fwfPhJkoh46LEKM6INbCsy8xXHT0YBQmbzXa2WAeOLNgppCepFCQq5SSrFPsV//Mi8zgv7zySdclU9lTyafWnfF7zOWNjxx6Vedi9rl5WNK1AEATmpc47YLd3s7U5GNGSrEumqqcKnULHBXlHX+ydIAjEaeOI08YN2pERrY4mTZ9Gk6UJh9dBeXc55d3lwfkPznwwWCDU5DRxfOrxTEuaxpc1X/JTy0+UdJawo3MHUxKncGrmqcG4mhAhQoQIcWwiiKI4lILkhx1BEPo5zkVRJDExkZtvvpm//vWvQOBGPS4ujgcffJBrrrmG3t5eYmJieP3117nggsCPcktLCykpKXzxxReceOKJlJaWMmLECNasWcOkSQHH5Zo1a5gyZQplZWXk5eXx5Zdfctppp9HY2Bi8wX/77be54ooraG9vHzAEdm+YzWbCw8Pp7e0d8jJHGytWrGDatGn9Ckd5bW10lDxGp2sH/r7YAqUqka6tCXS0lqKJqwDBj0qrJCHjAjLyzkHWd7Hl8/l46aWXePLJJ/H5fMyaNYs5c+Ywa9YsYmJi9rs/bnfnrugVazkeT/+MZkGQotFmodPmodPlodFkIpEM7gDy+eyU7LgV0e/hiSeayZH5KWhoxKGWMf+l5URJ3RC27xvGf1x3CePrtuHViqxvq+bhnx2sWbOGoqIizjnnHL7++mvSDQL3Ha9m9uSxJOWNg+PvBF0M+P1gqgW5BnRxsJ8Civ133gsNq6D0U3Ca4cwnQdr3Pl1WUO7d9XMk+WhzE59tbSU7VsftpxT0m2c0GklNTcXtdhMREcHmzZtxuVyYzWZ6e3sxm83Bx86/e3p6MBqNPP3004OKb8cC3d3d5Obm0tUVyJ1ftmwZO0hlY30buXEabj9l3AEL5yVtVdz+w1M0eBoAERDI1xfy+Am3EivX0PnssziLtxNx8UXoZswYsLzlhx/oefsdADQTjkMzfjzKvDwk6qHnfPr9XqzWHZh61mHu3YLfv6tgqFIZ1yeWH4dKNbT/m9/voqr6IRz2epSqRHKy/4ZUOry5o3tSV1fHH2fO5DS/iM3vY/KbbzJWJsf41pv8YYoNq9rPKXFzuGvmlYd1PzweD1988QWnnHLKoHUTPB4PjY2NQQfqTqRSKREREURFRREZGUlUVBRhYWHBz9M93z7LB23LUIgafjrnf3Td9y98JhPRf/oT6qLAyK3/+7//49577wUCHTxXXHEFAE8//TTXX389Oy8RzjrrLJYsWYJKFTi3v/fee5x//vkAXHzxxSxefAMNDS8gl0dQUPAA635YS3VLHekZ6f2imfx+P8XFxZSUlAAQHR3N9OnTB83t9vu9tLZ9QGfHMgB0unzS0q5GJjv0Ohg+n4vSsr/i89pISf0924s9NDU1kZWVFbxWGLg/fkwmE0ajkba2Njo6OvD5fCQlJTFjxox9dmIcDezvcxbi6MJut7N06dLgSKWEhATGjx8/rNeXO+NfNm7ciMMRiHrLyMhg7Nixwe/60YDD0c2KlZcTGxvLyKLHkcn2f83jdzgw/uc/eFvbUGRkELvoZoTQKNIDwu920/n007hKyxDkcqKvuxa/w0HX8y8g0WpJeOB+JL+SY2p1uXjlm+/InDAZo89Pi9NNi8uD2esbtL1aKuGW9HjytEP7nrh8Lu5adRcWt4Xz885nZvLM4dz9Q6LN1say+mWsb1uPr28Ea7gynPNyz2N0zOihRfP5vTy84WGaLE2khqXSYGkA4MqRVzI2duxh3f/DidfvpdXWGoxl2Rnx8n9T/i/YZvHmxbRaWzkl8xSmJEyh09HJJ9WfsK0jEGWnkCqYmzqXeWnzUEp/eSPGoV4L/Br0jhAhQoQ4UI6+7u4+dhZGOuGEE4LTlEols2bNYtWqVVxzzTVs3LgRj8fTr01iYiJFRUWsWrWKE088kdWrVxMeHt7vRnjy5MmEh4ezatUq8vLyWL16NUVFRf2EuRNPPBGXy8XGjRuZM2fOoPvocrlwuXaJRWazGQj8IHl2zwc+RigpKWH27NlkZ2dz++23c94Zc+itfIr2zp9weCW4vEqitbkkpJ6LXj8OibWDyop0cpIKkEnfxCFpx9b6DGXeNcQlLWBHiYdFixaxuW9o585t7Mx5LiwsZPbs2cyaNYuZM2cSGRmJx2PCZivHZqvAaivH4+nqt4+CIEOtTkerzUOrzUWjzkAi2XXR7vOBz7e3Yy9HHzaepuZv6bH8TJovUIjGmJyOXhuGB2A//7eTLrmeqgcXEWmzMGV0NvnlJfzxj39EoVCwZs0a8qMl3HO8mhmTxhOTNxHPzL+CMjyw3s4KpN/dFViRVIGoTwR9EmJ4MuiTEQ2poN2jM8FtQ6hZjqTyq0CWOYBMha+jEqL6Yl8kyv3u95HA7fXzfakRv+hnTm7UgO9AZGQkF1xwAa+//jomk4n0Axg63dDQwPfff49uH8OCj1Zuv/32oGh+3nnnYcgczYbvSnA4KpkYuQOnMx2ZbGgXnl22Xm5fvpit1lJ8eAGIEmL485gFzE8Zi0SmxiuKhC9ciKq8HGVR0YD/g23lSnrfCYjmuvnzCTvjDARBwAf49vM5EkUfNlsFvb3rMVu24PPZg/Pk8ijCw48jXH8cKtUuF/3Qz4USkpOuprrm3zgcTdTWPktq6nUHVaxxqCQlJXH+XXfR/I9/ECGV8f499zLps09RfPAB2UY36+JMfF2/kn949l0k81DZeYx2P1aiKNLV1UVtbS2NjY14PB56fU42+JoxSnvxij78IvgdfiRGKTK5Ap/gQxT9ONw2RHy4cSIIEGNV0/vOO3i6uxCkMiTpacFtffPNN8Ftzp49Ozj9qquuIjw8nCuuuAKPx8PHH3/MSSedxAcffIBer+9XFPTSSy+lveN7/KKf8PCpuM1uDJXg7rXSINQzduxYZDIZTqeTNWvWYDQG4pxycnIYM2YMEolkr5+T2JjfoVSk0tzyOmbLDsrK7yYl+Q9oNJmDth8qHR3f4PFYUChi0WnHkpNjoqGhgerqagoKClCr1YiiiMViob29HaPRSHt7O263u996oqOjmThxIj6fL1jI82hlsM9ZiKMXuVzO6NGjaWhoIC8vj8TEwCig4f7/JSQkcMIJJ7B9+3aqqqqorq6mqamJ0aNHk56eflREBYmiCtGvR/SLmM0VhIWN3P9CMhmGP/yBzocewllTTefrbxB+6SVHxfs5FvA7nXQ/8yzuqkoEhZKI665FmpODxOdDEhWJt7MT84oVaGcducLzhxOJz0eK38NsvaqfmGjx+mhxeWhxe2h1eWh2eWhyejB7vDxZ18Y9mfHoZPsfCSVBwrT4aXxe9zlfVH/BuOhxv6iIKooiteZavmv4ju1d24PTM8IzsLgtdDo6eW7rcxRGFXJuzrlEqvYdKfh13dc09Dagkqkwu8z4/X7Gxo6lKGLgdeixRrwqnnhVPBNjJwLgF/393lOvs5ceZw9v7XiL7+u/5/SM07ki/wpqE2v5pPoT6sx1fF79OSsaV3By+slMjp+MVDJ8o+cOlEO9FjjW/58hQoQIcTActY7zVatWMW3aNJqbm/sJ2ldffTX19fV8/fXXvPXWWyxcuLCfeA1wwgknkJGRwbPPPsv999/PK6+8QkVFRb82ubm5LFy4kNtvv52rr76aurq6fgICBIT6V155hQULBs+4veuuu7j77rsHTH/rrbcGdc8d7Tz88MP89NNPxBoEbrwgmbGTIpEqwxAFFV5HJNaOyRCZjTo24H4S/YAQSAYx2CpJsn5Cb0QPJqWeypYeNm1s47PPzMSGJ3L5zIDIW2MWef3T1ZjtAWdTWJiEjAwFmVlKRo2MJCVFT1hYGGFhYUhlUhAl+MVo/L4E/P5E/P5Y4OCdcoLQQZvxCcz10eRsNOKVSeg99SqiEtKHtHx87yY+XVPN6NpiUPpp89Vz02cWRGB8goQ7ZmsozM+B6HzK48/Ct8dFcXrn98SaixHEgQJLryaNsoRdkUXpHd8RbS1F6g8INR6plrbwsbTrR+I9zE5ch8fNGx3FGCVm5FJlQLz0g8dtR0BAIkgQRAGJICAVA39L/XKklnEYFHIuzPIjGeTetKamZp/FBvfFcccdx+23395vNMTRTmVlJbfddhuiKKJSqXjiyaf4vjcaq8/I2JhypiWW4/WMwuuduM/1+EWRn/3NLLf8hFcInO+Ufg1TJXmc4E1Cv34Dgt9H58kn7zOqR1NejmH1agCshYWYjztuCNE+IoLEiFRag1RagyDsGsIsihp8vgx83ixEMQY4dEFCEDpQKj8DwTekY3OoeL1eltx4I8cLEhrdbpLuvIMspZJ6rZ9nfN8iInKyZCJTwgr2v7Jh2h+LxYLFYsHj8WCR+CjXmmmQGbFKTYjsee4QQCJBIpGCKCICot+/21yR+WURnOaJQFNZiSs+nq6TTgLAarVy2WWX4ff7SU5O5sknnxywP1u3buWBBx7A6Qz83zMzM/nTn/7Erbfeit/vJyYmhueeewC15mMQBZzOCzF0GYhsV7DOX0GvyklcXBwymQyj0YjX60UikRATE3NAHWGCYEKhWIYg6QVRgsczBZ8vn4P7zHlQqt5BEJx43LPw+XIQRZGWlhacTidarRaJRILD4cDr9fZbUiKRoFargw+5XB4S4kL8anA6nXR0dAQ7iNRqNdHR0UdFvR+5/Eek0kqk1nEoeiZjinbvfyFA0dJC9LffgihiPu44rEVFh3lPj30Et5uob5eh6GhHlCvomjcPd9yu2BVtWRnha9bg0+ownvO7AxtB+SvAA7yjiqRHkJLhc3OKu3e/v0Tb3dvZ6t6KFy8SJIxVjKVIceQ/i6Io0uRrosRTQocvUNtGQCBZlswI+QhipbF4RS/FnmJ2eHbgF/3IBBmj5KPIl+cjHSQuzeQz8YXzC/yinzhpHEafEZWg4nTN6aiEo2fkyuHCJ/qo9Fayzb0Nlxi4Ro+TxjFOMY4oSRQNvgY2uzdj8QfqcEVJozhZdfIxe+1gt9u56KKLQo7zECFC/KY4ah3nO9nzR2Uoudl7thms/cG02ZPbb7+9nwhoNptJSUnhhBNOOCZ/SJQKP01LnNgjtUSNkOKT+rF2h9FUmYfGMIlZp0wmKScCdVjgBsrj8fDtt98yf/585PJTcHedTckLd/D0T98webKclBQlf714+v+zd9ZhklTn276r2l2mx13XnXV3WFwCBIIHflESvhgJIYEEYoSQBEkgJIQYAYLburDuruOuPe0u9f3Ru7O77C5rMytQ93X1NT1dVafeaqk656n3PC95B7pQNbYCUArMmFaCx6bDY9fjMkUQ1Q0olEkgSGtbkM1bGqmtjaNRF1NRMY0pU2YyceLEHl/7s6G7u5vHHvsxl3SnvvrtOel8xbKBxITLwXryQjRCnZGMwG4WtprIDLvJLipmduluQjH44TQT48eOwdR/Ksnx91MkKhD2vIVUPO2ITPJ5qTsO/g4EbzN4mxA8TeBtxpE1jJKhBz3WIz4Ub/8LtFYkcy5S/yuQCiaQpejb6fWhaJivvP0Xdni2kxQDiIJIBAVIkJCSSIoTZ1QKCoHhFQV8a/i1jCiwnnA9lUrFO++8g06nw2w2YzanbpYc8mw2mUw9r/v9fm655RaCwSCbNm1iwYIFPP300xdFZzORSPDzn/+8x+ri0UcfxT5sFsKmbTiIccXQICoxA1F0UVE+5YRTz8OJGPeu/j2N3krQSKiiGqY4RvGzkV8i9NFHBDesB0lCUKoYMWIEqhNY2gTXrMVdXQ0ZGRimT8d83XUnfB8lSSIcbsDj2YTHu+kIiyQzCkU2FvNILJZL0OvL+yQj3O0ZSFPTX4E2cnNs2GzHerT3Jg379xN79jny1WqqV63i//71LwDefn03rYlmtopN/Hzed/ps/+FwmHfffZf09HQ6OztpJ8AOq5NWtRu/4Dwo4voBCQ16clVZ6JQaVIKIQlRgMznIzypBJSgIh0I0NByAuIAUjGBpdPIlTxxrRR5hjwfTvMsxXTo3dXxvv91jA/H1r4+hrGwNObm3YTQcLs46b948Zs+ezZVXXonT6aSmpoYHH3ywZ7v77ruPESPVdHdnYDaPID/vBnzv1ZIQogzO0lLprMNgMBAMBrHb7ZhMJiZOnHjKPuJHkkhcR3PLP/B6twKVWK1p5GTfctSso1Ohs2sB7e1m1OpSysu+0eOb3tTUxOrVq3vW0+l0iKKIw+EgMzOTzMxMbDbbBW/JciKOvmbLVi0yxyeZTHLgwAF27dpFIpEgFotRXl5O//79z9uN61gsxtKl+8nMdKEKhsgS87BMLUU0nNr3OFBWhud//yOjsRH75VegHTSwjyO+eEkGAjifeZaYAGJREfavfx114dH9Y2n2bNo7Okn6fQzMykJ3ySXnKdre43TPj8PDUX5e00ZQklBnDWV22qePUfRNepqrmolLcRSCgm5lN9PGTkOvOjeJVrFEjE0dm1jauJSOYAcCAjliDqMzRzMjfwYZ+tSNkSWNSwhFQnyr8Fv4o35er3ydKncVTTSRNCa5sfxGii3FPe3Gk3Ge2vIUDr+DIY4hGJQG1rWt4+5BdzMsfdg5ObYLhVA8xOKGxaxoXkEsEWMzm7ln8D1c7riceDLO6pbVLKhfwNS8qcwtnHve4jzbvsChGfYyMjIynycuWOH8UHGitrY2srOze17v6OggMzOzZ51oNIrL5cJmsx21ziFP1aysrJ5p4UfS2dl5VDvr168/arnL5SIWi/Wsczw0Gg0azbHT7FQq1UU5KO1s3kx6ZxA6g8RqRGrysnlvo4fVO/5EOPpbiv9ZzEMPPcTtt99+1PGpVCoWLlzIt7/9baqqqgAwqIZzjVbC7E/58fqNKkJGDZbuIOpoErsziN0ZpBiIK+y4rFo8Ziv7u+L8/c1VxBIJYDuwnd/+9g8oFApGjx7N9OnTmT59OhMmTMBgMJz2Mb700ksEPMVYu71IgkBZoR3RkIaYVnJqGTNlMyiuWUpLgQHrgR0om2PcN86CRSMwbtw4jAPnwLivoQi7YeXvoasSOnbDnMeOzuy156ceJyIqQb+5qcKk2cP7vOBnLBbj/956ng3OzSQFH4ggSGoKw3kU55cjGY1sq28lFu1EQkKSkkhCEkGQEASJmCJCUtFNtX83o4ru+dTB9Te+8Q2+8Y1vnHJcDz74ID//+c+JxWK88MILlJaW8v3vf7+3Dr3P+Nvf/sbmzZuBlC3RrV/+Gj99dwuxaCdXDqynouRu2js+IBxqwuNZSVbWVcdt57kDi2n0VqEQVVxZMZf7CiZjWLcN9y9+iRSNIiKgHzMayzXXoExLO24bgbVr8f73v4iCiHH6dKw3fuG4onk43ILLvQG3ewPRSGfP60qlHotlJFbLJRiNA3oK7vYV6Y4JxOMddLR/QFvbfzAYcjAYyvpsf1/+5jf57jPPMkwQ8C5dRkdHB7m5udzSfw6/2/US7Yk29nXVMSS7vFf3293dTU1NDbW1tWzpqqUmuoNWsYuQwoMggF5tQoUKhyGHbKOFKwrHMK/fZIjFkGJxSMSRkkmkWAyF0Yh4cKZTcmCQaFMT4R078DUvQV1RTqy2NvX5Dxncc/5etixVQFYQYMRIkXi8m8aGZygovBerZVRPnOPHj2fVqlXMmTOHxsbGo+xK7rrrNrzeZxAFkYz0GQjuOJIvjkKtpN+YgVQvaCAYTFn6FBYWMnbs2DPOXlWpVJQUf53OzoW0tr2B17OBaLSZosKvodGcWhG6RCKCq3spoiCSnXUlavXhbLjCwkKam5vx+/1kZWWRmZlJenr6GRVhvJC5WPsoMueOIUOGUFxczMaNG2ltbWXPnj00NjYyZsyYT+0X9yXJZAaiYj8xQzuCR4LuGCrrqYmOllmzSLZ3EFi1Cs/Lf0f7gx+gugCLoJ5vEj4fzqefIdHcjNJkJv3b30Kdl3fsiioV5hnT8b73PsGlyzCNG3dRJDScCqd6fixTqbg118E/W5y83ulhoMVAke7E1ivTCqaxonkFrrALCYlIMsLyluVcXXZ1b4Z/DMFYkJXNK1neuBxfNJXxbFAbmJw3mal5U7FoLEclihk1Rt6vfZ8tnVu4tuxavj3q26xvW89bVW/RFmzjj9v/yMTciVxdejV6lZ7FtYtpCbZg1Bi5ZeAtWDQWphVOo8Bc0KfHdSGiUqm4rt91TC+czvs171PpqmRoxlBUChUqVMwsmsmEvAkoRSWqPk6EOtV4z6QvIPcfZGQrdmD7AADXD0lEQVRkPo9csKPB4uJisrKyWLRoESNGpIqKRKNRVqxYwa9//WsARo0ahUqlYtGiRT1FylpbW9m1axe/+c1vgNSA3+PxsGHDBsaMSU35X79+PR6Pp0dcHz9+PI8//jitra09Iv3ChQvRaDSMGjWKzwvXffFB/lK7m4z9tegDMQprm/lKlsjlw8fywtJ97Kup5ctf/jKPPfYYP/rRj7jllltobm7mqquuYv78+QDMHDeYq0u15DuDCH6IqURaKzIYdbUDg9lMLBpn97YI3v0u9E43FmcAVSxBujNEujNEGTD3sgG4rXq6dRZ2twd5+d3VJBIJ1q1bx7p16/jlL3+JSqVizJgxPUL6+PHj0Z2ksGE0GuXpp5/mB9NykYDObAM3lYWh37xTn2YqCHDJ3Xyz6UH+22whJ+AkYSpg2oQSlAMuh1F3QfNmWPccRAOg0sGAK09f+Dakwegvn942Z0AikeCb7/yVle3rSQielPWOpKLQ5eDB+jDFgg/7uGEYxozhQLuP5g3b4K3X6NBaaFWb8ZnT8JvskG9nfuAvBGMdvF65g5v7914hoKFDh/LCCy9w1113AfCDH/yAgoICbr755l7bR2/T1dXFD3/4w57/n3nmGV7d1Igv0ECxzcP4slKs1lSGVn3983R1LSE9ffYxxTBbvR38c/s/EVUiNw26ka+mjaTzid/h9XgAUJeUYL3hBjQlxZyIwLr1dP/jnyBJGKdNPUY0j0Q6cLs34nZvJBxu7nldEFVYzMOwWsdgMg067azesyUr82oi4VY8ni3U1T1HefmPUKsdfbIvk8lE0Re+AG+8wTC1mj898QSPPvoocz7ayV8dOjz6EL9Z+w/+ed3Pz3pf4XCYuro6qqqrWdO5n11SK22KLiJ2H6lCryl7FSNWLiu4jC+UT6HCnEnc6SSwZg0drz9OrKXlmHZtX7wZ40Gv2WhjE51PPdWzTJnmIHqgElGvR1VweDC7aNEiAPLztTjSUjMeJClBff3zJHK/RFra4eJl/fv3Z/Xq1T1FtyHliW61tuH3h1FrMjAaB+DfkYpNXWjGlGbH4XDgdDoZPnw4/fv3P2txRRAEMjLmotcXUV//POFQMwcqH6Ow4MuYzUNPur2zeznxuA+1Jh2bbdxRy0RRZNKkSWcVn4zMZwWj0ci0adNoaGhg8+bN+Hw+lixZQklJCSNGjDhu4khfIklWFAoDSY2bqLKNWLsDTcmpzVwRBAHbzTcRb28jUllF13N/IvMH30c8gwSMzyoJt5uOP/yBeGsbCouZ9G9/G9URiUufxDh1Gr4FC4k1NhLZtw/tgHNjZ3YhMTvNzG5/iC3eIM82dPCzslx0iuOPJ1QKFfNK5vGfvf8hloyhQMHyxuVMy5+GRXP6M7BOhivsYlnjMlY1ryKaSN3stmqszCiYwYScCWgUGvY49/DSrpcYnTWaibkTARiUNgi71k53uJt/7/0361vXc3P/m/nJuJ/wdtXbrGtdx+rm1Wzv3M6M/Bl8VPsRADdW3NhzHJ9H0fxIbFobtw28jUgi0iOQJ6Ukv9/ye4Y4hjA9//i102RkZGRkLlzOq3Du9/t7MpQhVRB027Zt2O12CgoK+Pa3v80vfvELysvLKS8v5xe/+AV6vZ5bbrkFAIvFwj333MN3vvMd0tLSsNvtfPe732XIkCHMmjULgAEDBnDppZdy77338vzzzwOpqeVXXHEF/fqlpqPPmTOHgQMHctttt/HEE0/Q3d3Nd7/7Xe69996L0nLlTDEYjHz78bfYX1fPW09/n4L6/egDMYqb2vj5QDtNM/rx56V72V9Tx3333cejjz7a41mbaTPx/RvGUNTchsIZRBIEukqzmTq6hOuVLrztISTtCIy5M7lkxDBQpkS4SCTMyuVvUbnkDTStjVjcQZTxJI4uPw78VABXXDmYboue/QmBtTVu1q7fTywWY/Xq1axevZrHHnsMjUbDuHHjeoT0sWPHHjOoe/XVVzGYRPK7AwDoRhjo0gcxl0w7vTcqrRTH4Fl49q3FvsuHuilGU/5ciobfBltehv2pTiT2Upj4LTCdn+ysTyORSPDn7Wv5x4438EWaQJAQJCW5bgffr4vST4ijcGSgv2Q0mtJSACoyTWTro7jVccqSThLBLkKeSoLROIodIlvHZNCqbeH5DW/0qnAOcOutt9LU1MTDDz8MwB133EFOTg5Tpkw5yZbnhwcffBCXK2Vvcuutt2IuGcbmD1eCFOLyAZ3k5d0HgMUyEo0mk0iknS7ncjIzLjuqne8vfpp4Iow6oeOu0skoVTpEoxFUSqzXXotu5MhPFSIDGzbQ/fLLIEkYpkzGetNNRxTtdFNf/wKBQGXP+oKgwGQajNU6BrN5GIrzWLhKEATy8+8mEu0kHGqktu4Zykp/2Gcx3fXDB3nqtVcpVCjZ98or+B55BCkaZWKrmg+LQ+wNVOGLBDBpTl9oSSaTtLa2su/AAZZ37GMPbbRIzYSUXiQpCUgIElhEO8Nt5dw94mqGZlcgRSKI2lRGdLyrC+8HHx7dsCAgKBXwiSJTolaDMjMTQalENJt6MtE1/fohHLxJWFdX13P9veyywSiVSkzmoahUFrqdK2lq+ifxhJ+M9MM+nPn5+axcuZI77rijx/u8y5mqDeJImwpxiWhdagqvttwKwIwZM4jFYie9sXm6GI39qKh4mLr6PxMM1FBb+zQZmZeTlXnVCe2DEokInR0LAMjMuLzHokVGRub4CIJAYWEh2dnZbNu2jaqqKmpqamhubmbkyJHnuHiogF5XgleznbCqEX376RUIFpRK0u67j/Zf/Yp4RwddL75I+je+gXAR1U3pK+IuF52/e4p4ZycKmy0lmmd++iwehdGAYdJE/EuX4V2w4HMpnAuCwL156TxU2UxbJMbLzV38X376CX8TY7PGsqR+CW2BNhAglowxv3Y+N/W/qddiavY3s6R+CZvaN5GUUpZq2YZsZhfOZmTmSAA2tW9icf3iVBxAIBZgQs6E1A0mrY2fjP8JyxuX82Hth1S5q/jl+l8yq3AWN/W7ibHZY3l1/6u0Bdp4t/pdAApMBWxq20SZtQyr1tprx3Kxc2Tx120d26hx1xBNRJlVMOs8RiUjIyMjcyac1+Kgy5cvZ/r0Y++63nHHHfz9739HkiQeffRRnn/+eVwuF2PHjuXZZ59l8BGFfcLhMN/73vf4z3/+QygUYubMmTz33HPk5x+2weju7ub+++/n3XdTF/irrrqKZ555BqvV2rNOQ0MDX/va11i6dCk6nY5bbrmF3/72t6eVUeP1erFYLJ+JYhnJZJJX5q+gbvGfKWrYjyEQQwBiCoFdViMvLKuitcUJwI/umssQdyu6UKqImseqI/P2B5h72a2prOu1z0HzpsONK1Qw4CoYeuMx+3V1d/LBv57BXbUZQ3cHFncQReLwV1QCYmoFbqsBp8bM1kYvr81fd0w7Op2OCRMm9Ajpl1xyCWPHjuWW4XrKW9140rQMv1UCUxb9x/4DzelmsoY9xN++n2febcLh9tFcUsJ3LitC6alNLe9/OQy7BRQX3qSOn8z/NwvaNhKKpyyMEkmR7G4r36mLMhgl6sJCTHNmoxs+/JgBZTIYJNbaSqy1jVhrC/G2dmJtrSSc3byWpuVv+Y2Agv9e9WuG5BSddayxWIwPP/yQefPmoVQquffee/nrX/8KgM1mY82aNfTv3/+s99ObrFu3jvHjU57cZrOZHbv38tTKapo7DzCxoJm7Z1xzVGZsd/caGhtfQqk0MWDALxHF1DlnX3s1tyz8IUnizMuYzi/mfhMgNbC1WBBOYncR3LgR599eSonmkyZhu/WWI0RzD9XVTxCJtIMgYjIOwGodjcUyAoXiwipsHI12U1n5OPG4F7N5GEVFX+8zoebRG28ifc0avIkEym9/i/8bPYam11/h9tEuYuokdw38Et8aee0pt+fxeNixfzev71vJ3mQLTkUXKBKICgVKhQIQMEb0XJLWnzKflduvuJFkbS3BrVsJ79iBbuQobDelzpNSIkH3y/9AO3Ag2sGDEPX6HhH8ZHQ8+SSRyipst96CcfJkAP7yl79w332pGzh///s1DB/uIDf3FtLSptHW9jYdHSmR3pE+i5zsG4/7ngcCNVRV/RJBVDJwwBMkWpL4ljaiMKuxXlt2TgS1ZDJOa+vrdHUtBcBoGkhhwZdRKo/1m+3oXEBry/9Qqx307//Y5044P/J8Kk+1ljkTOjs72bBhA56Ds54yMzMZPXp0n/d5D313R48W6Wh/B1VtHpneL2D/Yj9Ezen1s6JNTXQ88VukSATjzBnYvvCFPor6/CMlkySDIZLBAFIwSDIUIhkMph6BYM/z8O7dJFwuFGl2Mh54AKXj1PrEcaeT1od/AskkmT988Bgv9IuJszk/HgiEebymhaQE9+WlM9l+Yr/zbR3beHHni0QTUURBRK1Q8/C4h0nXp59wm5MhSRKV7koW1y9mj3NPz+vltnJmFc5ioH0g4USYNc1rWNq4FE8k9fvVKDRMyp3EtPxp2LS2Y9p1hpy8duA1dnftBuDS4ku5ouQK4sk4ixsWs6B2AVqlFpWoojvczfic8dw64NYzPo7PMkkpyca2jdi1dsptvWv7d7qcbV/gs6R3yMjIyJwq51U4/6zxWbyQ1LZ08fKb81HVvk9hwz5M/hgAcYVAU1Y65lAA+8EM7ohGQeeAYdzzkxfQHpldKElQtwpatkD7bgh7YNSd0O9gdq2/EzY8D5mDIGMQ2Et6BOfOjhY++s+zRLtqUDXWYXL6jhLSASJqBW6rEafGxMbabt5avIlPotVqyc2w8uuhDpTxJN0jy5gyzoU/t5iM7KvIzr7+9N+c/R/x2vuvEV66B0GSqCwo4mezjDDu65B34Vn8/GzRq7xRuYyY2IUgCCiVGirSxvHjCTdQsHQFCVc3plmzUJedvuAVa20loVYz5rXvkRC8VBhG8tZtPzrrmD/ZuYvFYlx55ZUsWJDKGi0qKmLdunXnzXP1kyQSCUaPHs3WrVsB+P3vf0/OxKt4fd0qzGofP5yjp7TozqO2SSbj7N//MNFoFzm5N5PumAnA9a99n+pIFcaojjeFGdivvQ6l7diBzfEIbt6M869/g2QSw4QJ2G770hGiuZfqmt8SCbeiUtkoLf3uKftDny8CgRqqa55ASsax2yeRm3trn/is79uzh7dnzsKsUPCuKPDWjh04f/oIvywMsjU/TJ69gtdm/vhT24hGo2zZt4N/7FzI3lgLboWTJPGDS5NoVHouKZjA9OyRzMsbji4J/h072PrfVylJJCAc7mlLlZND1k8ePqtjSobDNH/nu5BIkPWzR1FlpD7rG2+8kddffx2VSmDhoi9gs5rp1//naDUp39/OzkW0tLwGgM02jry8O455zxsaX8LVvQabbTwFBXcDEO8OkwzHUeccv9htX+Fyraex6R9IySgqtZ2iwq+g1x+2MEomI+zd+0PicR/5+Xdit088p/FdCMjCuUxvkEgk2LdvX0/xUFEUGTx4MAMGDOiz4qGHvrtTp5ZRX/97kq0CeW33Y55RgKbg9PvbwS1bcb7wAgC2276EceLFfz6QEglijY2EKyuJVFYSrakl6fef8vbK9HTS/98Dp9zPOITzby8R3LAB/SWjSPty31sM9hVne358p8PF/9pcqEWBx8pzydYcP7lBkiSe2PQELf4WLGoLzrCTSzIv4c7Bd572PpNSku2d21lUv4gGbwMAAgLDM4Yzq3AWhebDNzJe3Pki2zq2AaR8yPOnMTFn4kmLk0qSxPbO7SysX8g3hn/jqPWDsSDv17zPx00fY1abeWjcQxhUsv3RhY4snMvIyMicPhdeOqzMBUVxjoOffO0W3l1RQaC5iqrKJWTt3YLRF6WoOVX4MykItBbmMPeB31FaMfjYRgQBiienHpIE3hbQHCGqtO+EtoMPAKUG0gdA5kDSMwdz+/0/67Ei8Pk8vPXXX+Or3IrB1YXFE0ITTZDZ4SETDwOVcNPVQ2g3aamMKdjXGcflkcjMzGRKMSiq6wjqVdxcriJqm4xf0YGzexWZmVchiqfZeSifw43/dwmPSM9TuuRdKurr+PHKITx2w4Ulmj+x9E3+vW8xUbEDQQRBEnH40nlixm2MGZXy+JVuOn5G6amiys5GBfQ392O3byNV/kpisVivizMqlYrXX3+dKVOmsG3bNurq6rjiiitYvnz5GRWL7W3+/Oc/94jmQ4cO5frb7uGH/1uElIwyr383Rfl3HbONKCrJyLiUpqZ/0dkxnzT7FNY37KImUgMSXFOjJtK9GenSy+AUBrTBLVsPi+bjxx0lmsfjPqprnryoRHMAg6GE/Lw7aGj4K93dqwiHmyks/Apqtb1X99N/4EBCAwdg3n+AIeEw/33rLa4ZP45b1q9ka5aPZm8NG7tqGe042lNekiR2N1Tzyv6VbHDupDPejEQSFAASStTkqDKZlX8Jd426GrP28Pmv9aePEG1rRd/RQTIjA6XFgm7YMHTDh6M9aCd2NkQqKyGRQOFI6xHNE4kES5YsAWDwYBsWiwmV2o5GffgGVHr6bJRKU0ocd60jHvdTWPiVHquceNyP270RgLS0aT3bKe2Hi22eS2y2sWi1udTVP0c00klV1a/Jzb0Fu30ygiDgdK5IeZurHVitY89LjDIynwUUCgWDBg2ioKCAjRs30tbWxo4dO6irq2PMmDFkZPTdNUWnK0IQFCS1AeKim4QzA85AONePHEHsyivwvvc+rn/9G+CiE8+leJxoQyORykoiBw4Qqa5GOuLG65EIGg2iXn/EQ5eatXTwf4XZjG7ESBTG0+9HmebMIbhhA8HNWzBf1dFznfm8cWW6lT3+MHv8IZ6p7+CnZTmojzMrTBAEbh1wK3qlHn/Mz683/JrN7ZuZVTiLPNNxCrEeh1gixrrWdSxpWEJXqAsAlahiXPY4ZhTMIF2fTlugDW/Ui1md+n1Mzp1Mq7+VmYUzGZ01GtUpjnkEISXED0sf1tOXlCSJF3e+SIY+g5VNKwG4uf/NsmguIyMjI/OZRRbOZU6KKIpcM30MHk8/1q1Lo61kKjt2LaCgaT8xtYqMubfztVu+emqNCQJYco9+LXNIKgO9fTd07IWoH1q3pR4Ak78L+aMBMOm03H7/Yz3FPJvrq5n/32cJ1+/B6OrC7AmjjSQojAQoAGaqIVygxGX1YG9OVZNvzy9Bd+MLaJMxVA1PEou58Hg2H1Mo7uRvjAKM6fz0Ow/xUH0NA6t20f/ATn764Ld49Fd/OL22+oDnVn/IX7Z/SERsOyiYC9j8Du6tSzBLp8MmHM6G6S1Lhcdn38o1b24lKfj45bL/8ZM5X+yVdo/EZDLxwQcfMHbsWJqamti0aRNf/OIXeeutt/os2y0YDNLR0XHcR3t7e8/z/fv392zz7LPP8o/V2wiHuymzu5g1/Lpjin8ewmabQFv7e8RiblyutTy29g0kklgjBq7vUmOYMBZVTs6nxhh3ufC88w7B9RtAktCPHYvtttuOI5q3oFRZLhrR/BA22zgUCj31DS8SDNZSWfkYBYX3YTL2rlXPZT/6ERtvv4NitYZ//uY33LJkCQXLV1DkNVGnifD0llf5x5wHAVh5YBN/2/IBNYkO/KIbSPmJSiRRS1oKNNlcWTaRLw27HMHvJ7RjB+GX/4P05Xt6bJC0AweSiEbxO9IZePtt6MvLT9mC5VQIHyzkeaT/7NatW+nu7k4d77zBiIKAyTjgmPNA6j03UFf/J3y+XdTU/o7iom+iVBpxudYiJWNodXno9cVISQlBPFdex8dHp8ujovzHNDT8Da93O01N/yQYrCE7+wY6OlIFrDMyL++T2QoyMp83TCYT06dPp66uji1btuD1elm8eDFlZWUMGzasT4qHiqIana6ApKka1UwJXc6Z21uY580j4XYTWLkK1z//RTIYxDx7di9G27tI8TjR+vqUSF5ZSaS6BikSOWodUa9DXVqKpqICTVkZSocDUadDUPbdOU+dl4t20CDCu3fjX7IE2xd7v993MSAKAl/JT+fHlc00hKO80trNHbnHt7zJNabGQTatjVGZo9jcvpn3at7jq8M+fSwViAVY2bSS5U3L8UdTswn0Sj1T8qcwNW8qJrWJGncNb1a+yc6uncwsmMm15Sl7uQpbBT8e9+Mz7u8fud3Orp1s79ze8//orNEMTf/04tz+eIJIUsKuUpzVmCMpSbREYtSFItSGIjSEomhFkRK9hiKdhhKdGotKvsbLyMjIyPQu8pVF5pSxWCzMnj2bRWs2s6c9wG7HBCLROOPShhGNxVGfaUfFmJ6ybel3WSoj3V2fEtHb90DnXsg4Qhjb8y7s/xAyBhJN64+kyGL4rFvp6OjA5XLRFfLibduNuq0Ks7MTszuMNhQnJ+RK+aOrRPpf+mXQ2xEAe9pk2tvepcu5/PSF84MIgsCPfvUMv37gHvo1VlO6Yx2/fPwn/PChn53Z+3GWLKqr5DfrXqXZswNBTKaKDgbSuas2yRWODEw3z8IwduxJPbJPF0mSsL76No6InS5jBx/Urucn9M0AKicnhw8//JBJkybh9Xp57733uP/++3nmmWdOqUOeSCRwOp2fKoIf+fCfxnRngDvvvBNVdgnbti1GISa5aUwuZvPAE64viioy0ufS0vIa8/e/SnO8FST4UpUapUqF+YorTrhtMhLBt2ABvkWLkWIpKyXD5EnYvvjFHgH2kGgeDjWjVFkoK/3eRSWaH8JsHkpF+cPU1T9HONRETc3vyM66jvT0ub1282fczJksys4ira2dvPZ2Fm7ezNgB/ZnVsIen9T52Bjdz66sPURNpIyh4e7ZTiiqs2kzGZI1kakZ/ZhWOBJeL4LZtOJ96imhNber8BkSqqnqyya3XXoPh2mvY/NFHqEtKelU0hyOE8yNqASxatKjn+cgRqYG90TTouNubzUMoLfkOtbV/JBiooar6CUqKv4XTuQIAR9o0kMD9dhVKhw7DmCxE7fnrWigUeoqKvk5H53za2t6iu3s1Hu82EvEAarUDm/XMzvMyMjLHIggCxcXF5OTksHXrVmpqaqiqqqKpqYmRI0dSWFjY67UODIYygsFawlI9gjD5jNsRBAHbLbcg6vT4Fi7E88abSMEg5quuOocFT09MMholWltL5EAlkaoqojU1Pdf4Q4h6PZryspRQXlGBKje3168hp4JpzhzCu3cTWLMW8xVXoDCd2OP7s4xNpeS+/HR+W9vGYqeXQUYdl1g+PQt7iGMI61rXsbtrN9Xuakqtpces4ww5Wdq4lLUta4kmogDYtXZmFMxgfM541KKaHV07WFy/mNqDtZYEBPyxw33X3vxOD3YM5sZ+N/Ju9bsYVUZuqLjhU9fviMR4uKqZYCKJWhRIV6vIUivJ0KjIUqvI1KjIVKtI+4Sonjgkkgcj1IUj1Aaj1IcjRJPHusxu8wV7nttVSop1aor1Gop1qYdJ+fmqaSIjIyMj07vIwrnMaSGKInMnjSY/L49XP1hC1NnJ1m1b2VtZzaVTJzBhyLEdvtNCEMBWlHr0vzwlNB3sREWjUTpr99PepaKjuYHuWKoafAIFUYWeoKAnq6CcYUNGkpmZya5uePfjjeQ1fYTVU48x4KUtu4SBkRjJZBJRFEmzT6a9/X2CgWpCoUZ0uvwTx/YpGA16vvPki/zx/lspaWshc90innvWxte+/sDZvR+nwX+3fMzzuxfhjFQBIIgiRn8at9Uk+UJRGeb7ZqEdOrTPBoSCIKCwmLl+p8DzRvBLLWyo38+YwrO3mzgeQ4YM4c033+TSSy8lHo/z3HPPkZ2dzaxZs04qhHd1dZFMJns1HrVaTUZGBiNGjODxX/2GRz9cgZSMMbXMx5DS20+6vd0+hY6Oj3jL5UWlVpHu1jPXo8Y4cyrKtLRj1peSSQJr1uJ59x2S3tRsCk1ZKarLLkNfVnaEaO6nuuapg6K5+WCm+YXhCX8maDTplJc9SFPzf3B1r6G19Q2CwVry8+88YUb/6TL6q1+l5qePcIlOz9O//jVzXn6ZL3in8MLO3xMiyK7oATj4MzJIFso0Odw/5TYuyU4VfArv30/Xr35NrKnpqHbVxcXohg9HecRUdkGtRviEGNJbxF0u4q1tIAho+h0rnJtMItnZGhCET83cNxhKKSv7ATU1TxEJt3DgwKMkEkFEhRardSyx1gAJb5RkJIFRde5Fm08iCAKZGZeh1xVR3/ACiXhKPJCzzWVk+gaNRsO4ceMoLi5m48aNeL1e1qxZQ21tLaNHj8Zo7L2aB3pDKXQuIhCsOuu2BEHAet21iHo9nrffxvvRfJLBINabbz7n4nkyGCRSXUOkqirlUV5fD4nEUeuIRiOa8vLUo6ICVW7OBSHyayrKURcWEq2vx798OZYrrzzfIZ03hpn0zEu38GGnh780dVKoU5OuPr4tyuL6xbxd9XaPbco71e/wwMgHej7TRl8jS+qXsLljM4dKkuWZ8phVMIsRGSNQiAo2tG5gft18OoIp+0yFoGBs9lhmFswk09A3fT1REJmSN4XxOeNBApXixLYvkiTxl6ZOgolUnzualGgOR2kOR49ZVyUIpKuVZGpU+OIJGsLR44rkGlGg6KAgXqhTE0wkqQ1FqAlGaI3E6I7F6Y7F2ew9LKY71MoeEb1Yp6ZYp8Egi+kyMjIyMqeIPIKUOSMGFmXzw3u+wJN/ew1fMEwk4OOdDxeweksO91wzA7vl7IuFxGIxOjs7e8TP7u5uIrFCApKNZNKHEPehT7ixCj6yhSBaRRLFsLsYU5aauqsIbcQ3KA/HrMcpSjeQ27aEDxatINC8l9bWS8jNzUWlsmKxjMTj3kSXczn5ebedcbxWk4Hbfv4srz14L7nOLhIL/8f/MjO54YZbzvq9+DTe2bmOx1e9SkBoAiQUShWZhgF8fdT1zAknUVxjRFNcfNJ2egPD5Mlcu2YtL8XMRFVefrPuPf7XR8I5wMyZM3nxxRe58847AXj44Yd5+OGzK6Z4JHa7nYyMDDIyMsjMzOx5fuTj0Otms7lnsPP3j1fT7fNg1YW5edKVPb7Qn4ZCoaFKMZLG4FuIgsA3a/QIWjXmyy47Zt3Q7t143niTWEsLkCrqZb7maqqVSnZt347uwAEuu+wylMoE1TW/IxxqRKk0U1rynZ7ijxczoqghP+9ODPoSmptfwePZQjjcTFHR19BqP93S5lSYfeedPPnEbzH6/Yjbd7Clo4Nx48Yxds9AViZ3oE8aqdDmcvfIK5hYNoJobS2idNjbW9RoUqK5KKKpKEc3fDi6YcNOu+jamSJJEiQShPfsAUBdUNDjXRsMBlm9ejUAEyYUoDfo0ekKEMIaIk4v6nzTcS1XtNqcg+L574lE2gGwWcehUGgJVDYCoCmxICjOv3B+CJNpABXlD9PU/G8EBDnbXEamj8nMzOSyyy5jz5497N69m9bWVj744AMGDx5M//79e8VOzaBP3aAMuhpwLd2PNtOBbtCxN5dPB/OlcxENelz/eQX/io9JBkPY77i9Ty1OEj4fkcoqIlWVRCqrUtcM6WiRUGGzoSkrQ1NRnrJeycq6IITyTyIIAqa5c3C+8Bf8y5ZjmjMHsQ+sei4WvpBpZ18gTE0wwp8aOvlRSTbK41xXBzsG807VO0QTUSQkatw17HHuQSkqWVS/iH3d+3rW7Wfvx+zC2fSz9TvqO1Dvq6cj2IFOqWNy3mSm5k3ForGck+M8FZ/0RU4v+wJhNKLAz8tzERFoj8Zoj8Roj8YP/o3RGY0TO5hh3hI5nExwpEhedFDwztKoEE/wOwgnktSFI9QFo9SEItSFUmJ6VzROVzTORk+gZ91MtYoinZoS/SERXoP+AurDyMjIyMhcOMjCucwZo1CIDM41M37yVfxvyXrqa6sJuztYNP8jBgwYwMCBA1GexqAjFovR1dVFe3s7za1ttLR3EozEMWqU6NSpwZak1HEgriGmKyVmMYGoII9Whiqb0BkUZFgOZ5zm7f87Nwe6wGcD9wDo2EeJPsE+pZ4DBw6Qm5vyGHSkTcPj3oTbtZ6c7BvOKmu1OC+XGd/9BSt/9V3SPV46Xn2BnSNGMaS098Xj+Xs28ciKV/DRCEIqk0MftvPVoincfc2Xen1/p4K6qAhVfj5jPJ2syQhQF6wmkUj0mfc4wB133EF9fT0//elPT7quRqMhMzPzhCL4kUK4w+E4o+KmDV2dzN9RCcDNo7OwmctPabt4PM4/G6sAgWJRQ6nSiWnKbSiOyNSLNTfjfuPNHkFU1OsxXz4PcfRo1m7YQEdHKuMoFAqxdu0KcnM3HBTNTZSWfqdXROULBUEQSEubilabT339n4lE2qms/AV5+Xdgs44+q7YVCgWlN91E+1//yhSjgd/+5jc8/eyzjBXymagu4qorroCGBkKbttH64mskPB4ME8Zjvz01s0BVWIj97rvQDhx0RsXWjocUixHef4DQtm1Ea2uRYjGkRBziCaRkEhJxpHgCKZE4JktRc4RNy8cff0w0msr0mj27HwIpf3Pfymbi7UHU+SZM0/KOK4Cr1Q7Kyn5Abe3ThMJNOBwzSEbiRBtSMx60ZdZeOdbeRK22U1L8zfMdhozM5waFQsGQIUMoLCxk48aNtLe3s337durr6xkzZgwOx/F9n08VlcqMWpNOKNCMr3UfYmzYWQvnAMbJkxG1Wpx/f5ngxo0kwyEc997ba7Z2cZfroO1Kynol3tp2zDrKjIzDQnl5OQq7/YIUyo+HbvhwlOnpxDs7CaxejWnGjPMd0nlDKQp8oyCDhyqbqQyGeavDxReyji1mnmXIYkz2GNa3rkeSJCQkXtjxAgkpdQ0XEBiZOZJZhbPIN+XTHe7mzco3GZk5kmJLKiFmRv4M0rRpTMiZgFZ5fopzn4j2SIxX21L1VG7OTiNbk/otZWpU8Ak3n4Qk0R2L03ZQUNeJAsU6Ddka1Wn9BrQKkf4GHf0Nh8dygUSC+lC0Jyu9PhRNifcHH+uPENOzNarDmel6DYVaNVpZTJeRkZH53CML5zJnjc2k52tfmMPe+lYqd+/A73aya9cu9ldWYcmrYNbogYjH8VyMx+N0dXXR1NLKgdomOrqcBCMxQrEE0XhKCE4o1PRLy2bcyAoyMzOJoqJ7dS0Fdj0Fdj2FaQYyTGMRP5nJEQuDIQNCbgi5oH4NAOU2K/sSdlpbW/H5fJhMJgyGCjTabCLhVlyudTgc08/q/Rg9fDiRb/6E7X94BIs/wLKf3I/5qb9TmJF9Vu0e4uOqXfxw0d9x09AjmGvDNua16/l/ky/FMnVKr+znTBAEAeOUyXz9v/9lnUNJNOHm+R1r+dqISX2634cffhiLxcL69etxOBwnzBI3Go19OgiVJIk/L1lMIplgYFaM6UNPfbry0+teoaptJ2qNklsL9ETMSYxTZgKQ8HjwvPc+gdWrUxlpSgXGqVMxXzaPNo+btQsXEo1GUSqVDBo0iJ07N+Pz/4vOzjgWaxYlJZ8t0fxIDIYSKioepr7+efz+/TTUv5AqCJl1/VnZclz+ve/y4r/+hTUCtQsWsGrVKuIdHWTUN9C5bPlR6wo67VHiiiAIGMaMOeN9HyIZiRDetYvQtm2Edu1CCoVPuw1Bp8Uw5vCNhCP9zQcMTI1cjaZBaEdk4JlfR7TRh3dxA6bp+YjqY294KZUmysp+SDIZRaHQENrrhKSE0q5FmdY7VjkyMjIXP2azmRkzZlBbW8uWLVtwu90sXLiQ8vJyhg0bhvosBGmDvoywv52IqpF4RzlSQkJQnP21XT96NIJWh/OFFwjv3EXn08/g+NpXEXWnd26TJIl4R+fBbPLUI+HsPmY9VW5uyqO8vBxNaSkKq/Wsj+F8IYgipjmzcf37P/gWL8E4ZUqfZuyfClI8iZSUjnst62vS1SruyXXwTEMH73W4GWHWU6Y/VtieVzyPTW2biCQjSFKqyLZKVDEhdwLT86fj0Dlo9jfz8u6X2dS+CUmScIad3Df0PgDSdGnMKLjwblIkJYnnGzuJJiUGGnXMtH+6771CSHmfp6tVDOnlWAwKBQONOgYaD/+O/fHEwQKjKUG9NhShKxqnNRKjNRJjjTtl8SaQEtNLjvBLL9Cp0ZyHWgIyMjIyMucPWTiX6TUGFGbTvyCLxsZGtmzZwoFmJ86qj9mwfTfXzZlCRV46lQ0tHKhrIupzkwh5SSaThGMJ9relMhaTCjVRlY2Y3ozBmkZhho1hJWmUFKcyNQzAd+acQva2SguzfgrxKDgrU8VGu2sxlU4nuypAa2srVVVVjBgxoidztaX5v3Q5l5GWNu2sxdVJE6cgGH/Lnke/hd3l5r/fuZvbfvMiOdm5Z9xmlcvJjz9+lZ1tK0FITWPURK3M7LLw0JzrsYwbc94HKZAaeNrfeJOCYAb1plZe272gz4VzQRD41re+1af7+DSSyTjBsJtle7ZR2R5EqUjy5emzEMVTEwbi8Tiv1S4GJPLIpUgfJamNEIgewBArpu3xx3t8zHUjRmC59hrEtDS2b9/Ovn2pqbw2m41Jkyah1ysJBP5OV5cTt0dDSck96HRn/r27GFAqTZSUPEBb29t0dMynq3MxoWAdhYVfQaU6synLWqMR+8yZuD/8kLkOB1u2bGGg3U5hOCVeiyYTumHD0A0fjrZfBcIZzE44Hgm/n/COHQS3bSOydy9SLN6zTGGxoBs+DO2gQSkhR6FEUCoQFApQKlN/FQoEpTLlca9UIqhURxWLOyScZ2WpSE/XI4gqDPpSRKMK85xCfMsaibUG8C6owzyrEFF37DlFEIQe+6FIpRsAzQWYbS4jI3N+EQSBkpKSnuKhtbW1VFZW0tTUxKhRo8jPzz+j/pbBUEq3ag0RXTNSUCLuDKHK0PdKzLohg0m//5t0PvcckcpKOp56ivRvfvNTC15KkkSsuaVHKI9WVZHweI9eSRRR5+f32K6oS8t6bUbShYJh7Fg8775Horub4OYtGMae/Q3kEyHFEiQCcQSFgMKU6mslw3H8q5pJBuMkgjGkcCpzW51vQjfE0WvfkVNlrNXIVm+Q1W4/r7V288OS7GO+72m6NCbnTWZ543LMajNzi+YyKnMUBpWBA64DvLb/NfY49/SsX2GrYFJu3/ape4MFXR4qgymLli/nOS64mRNGpYLBJj2DTYe/E954IiWiByM9Yrorluixj1nlSonpogC5mpTFS5FOQ8lBG5kT2cfIyMjIyFz8nH+VTeYzhSAIFBQUkJOTw7/nr6F77x58Licvv/YWoiiSPFgcxmZQU2DXo9PpKCzMoEERISMjg+JsO4VpRgrS9Bg1vfD1VKohc1DqcZByoYnW1laqq6sZMmQISqUSu208ra1vEgm3EggcwGg8e2uVicNG0nX/wzif/Cm5XU7++sOv8cDT/8L4KYOv47G9qYYfL/k3DfFKJCmGIEoow1amuOz89IovYh8x/ILqkIpaLYaxY7hpy3p+qY/T6d/H4v1bmNVv5PkO7ZSRJIlYzI874MYV8OAO+PAEA7iDQbyhMJ5QBF84hi+cwBtOEogKxBKHM5quGJJJfnrZKe/vkWXPE8KHKCn4+ZSvYUtuo7NzAe3t7+PYUkbS60OZno79jtvRlJXh9/tZvXgxTqcTgIqKCkaMGEE02kZNzcuoNS60OisdHRPZtKmWSy/td0a2MxcTgqAgO/t69PoSGhr/RiBQRXXN7ygr/R5K5ZkVprv0oR/x+oIFDLOYWVldTeakSWRdMhpVXi6aIwqwni0JlwvDnj04q6qIVdfAEYVrlenpKY/0EcNRFxef1W+9ra2NnTt3AjB37kDUKjVGQz/Egz6l6hwjlrlFeBfXE3eG8XxUi3l2YY8o8UnizhDx7jCIAprSc+OpKiMjc/Gh1WoZP358T/FQn8/HqlWryM/PZ8KECadt56bXlyEgEDW0ITkTxNqDvSqKasrLyXjgATqffoZYQyMdTz5J+re+1VOnQkokiDU2Ej4okkcqq0gGg0c3olSgKS5OWa+Ul6MuKUHUXlhWGr2NoFZjmjEdzzvv4lu4EP2Y0ad9zZIkCZJSj11YMpogvNtJIhAjGYyRDMZJBmNI0YMzLvvZMI5PzagTFALRJv8xbUYbfUQbfeiGODCMOrfF0W/IsrPBE2BvIMxOf4ihpmO/p3OL5rKmZQ3eqBezxoxRbeTFnS+yrWMbkLJsGZE5glkFsygwF5zT+M+ElnCU/7W7ALg1O+2ExVEvNMxKBcNMeoYd8Rm5YnHqDmWlHxTUPfEEjeEojeEoK0gltWRrVNycZWeEWX9BjclkZGRkZHoHWTiX6ROUSiV3XDGFhtGDeXX+x3S1tZBMJJEUKgyWNHILc7li3ABMJhOCIDBx4rmLLScnB71eTzAYpLGxkeLiYhQKPTbbWLqdK3E6l/eKcA5w9bQ5/GzlKgrXLaC4vZXffOfL/OiPL6M9hcHTvuYa7n/zeVqUDUhCDFGhxKDO4gsDrub/BozBaLpwM5WM06dzXVkZT256kajg5Ldr3jnvwnkiESEQ9uDyu3EHfLiDflyBAN5QCG8ogicUxReO44sk8EcgGFOAdLLOr3jwkUKpVFCRoeeG8ZeeclzesJ+FbSkroeHtJhx/fwPLvbfQJS4l2Lof97IDKNFhu+WLaMrKaGhoYP369cRiMdRqNWPHjiUry0xL67/p7l4FkoRSYWDUyAdZtmwXPp+PzZs3M27c56MwosUygnLtQ9RUP0kk3EJt3dOUFP+/UyrQ+knshYUYRo5E19xMicvFx2vXMvfxx3slzlh7O6GtWwlt3Ua4rhZLRweRjAxEQUSVn49u+DB0w4ajys3ptUHY4sWLe55PmpQafBuNA/CvbkaRpkNbakHp0GGZV4xnYT0JbxTvwnqs15Qd1wZBNKjQj8pAiiQQe+NGp4yMzGearKysnuKhe/bsobGxkY0bNzJ27NjT8zHW5iAqdCTUHqLKdjTtFhhydt7pn0RdUEDGd79D5+//QLytnY4nfoth4gSi1dVEqmuQIpGj1hc0GtQlxWjLy1GXlaEpKuo1f/SLCeOUKXjnLyDW3Ex49x50gwcds44UTxJt9pMMxEiG4qm/h0TxQAxNqRXjhINiuCAQ3N553H0J6qNvXgsqBcaJOYg6JaJehWhQkgwnCO3qIlLtQZ1/OHElGU0gKMResfj5NBxqJbPSzHzU5eG1tm6GGHXHfNdNahMzCmawtmUtAqll5dZydnftZnzOeGYUzMCh693vd1+RlCReaEpZtAw26ph2EouWCx2bSolNpWSEOSWmS5JEdyxBfShCzcGs9MpgqgDpU/Xt9DNouTnbflxbHhkZGRmZixd5pCvTpxRk2vneHddQ2dRBIiFRmutApTz3XoNHIooiZWVl7NixgwMHDlBcnCqw40ibRrdzJR7PVmIxzxlbPHySnzz8Mx76gY/+O1dT0VjL87fNpN2ips2ipd2mpSvNSFStJB5PQDxJKrdEIClEkVQpmwZlzMhc21h+ecN9fVpos7dQZWWhysrikvp1rOlaQVO4Bm8ogFnXd2J/p7uVTTU78QSDB4Xww1nhvohEICIST54sQ1jgyNOiIIgYNAJGjYhFq8SkU2HRaTHrtFj0eqx6I1aDCZvRgs1gQatSnLbI+ePFzxIlhDKh5Fu1SqTsGBp7HvbwJFzv/YtIMI5h5DyU5eVs2LCBqqoqABwOB+PHjyYYXM2+ffNJJlODeItlJNnZN6DRpDN+vIklS5ZQU1NDVlYWRUVFpxXbhUi8O0wyGEOdd+LBmFaTRUnJA1RV/ZpgoIb6+j9TVPT1M/I8z736Krx/+hNZnV08+ac/8eCDD2I6zVkjcHAWQ0MDoW3bCG7bdnRhOEEgmpGB+drrMI0aiTI9/bTbPxUO2bQoFFBckrqRoBfKCVe6odqDttSaWm7WYJ1XjHdxA/oRGScUFkStEv2QvolVRkbms4lSqWTo0KE4HA6WL19OTU0NFouFAQMGnHIbgiBg0JfiCW0nrGpC25GHlEz5Q/cmqsxMMr73XTr/8Efi7e1433u/Z5mo16UE8vKD1iv5+ReEXd75RjQYMEyaiG/hYrpffgvT5WaSwRiqTMPhbG9Jwres8YRtJAKxnueCSkQ7wH5QDD8kiKtQ6JUIqmP7w9py29HxaJSYJuZiGJWJqD38+QS3dRCt96EbaEdbYftkM73KlRlWlnX7qA9FWe8JMM567Cy42YWzmVs4F5UilZ09Pmc8ozJHYVSf2Yy588WHnR6qgxF0CpEv56V/5rKvBUEgTa0kTa1kpCU1pgkkEnzQ6WFBl4f9gTCPVrUwxmLgC1l2sjQXR7a9jIyMjMynI/fwZM4J5XkZ5zuEoygtLWXXrl04nU66u7ux2+3odAXo9cUEg7V0d68iM/PyXtvfY7/6HQ898BX6H9hGmj9Gmj/GwOYAEqlOmNugpNWsSgnq1tTfoEaBIm5gQKyQP932AHZH33bs+4LH597G9H+vQxJCPLzw3/zh6vv6ZD97Gnbxqw/XEooeTxg/emClUSkwagRMWgVmrQqTVo1Fr8Wq02ExGLDqTVgNZuxGKyadDkUvD8SPpMPnZI1rG0gwvtWIPanAcs3VCIKALToUz24vCSlJbFIFCxcuxO12AzBgwADy8/00Nv6CWCz1ml5fTE7OjRgMhy1iMjMzGTx4MLt27WLjxo2kpaWdkeh7PpESEslADIVZTTKawLe8kYQvin5YOrphJx6UabU5FBffT3XNk/h8u2hqepn8/LtPaxAXCoVo12hQ29MI79/PwFiMF198kQceeODUYk8miVRVEdq6jdD27SS6jygOp1SgreiHbsRwlAMGsm31KowzZ6DsI0sdSZJ6hPN+/czYbGaUKjNilxkIosrQIagO/35EvQrLFSVHCVFSPImglAtiycjInD05OTmMHDmSLVu2sHXrVkwmE3l5eae8vcFQhte3k4i2CYVKixRNIGh7f1ijtNvJ+O53cL3yXxA4aL1S0auzgT5rmGbNwvn6G0Q69+JeXoXSXoAuNgBhwAS02mxElRpVtgFBrUDUK1EYVClBXK9ENKiOqa1hHJt91jEdKZpLSYlYUyrjPbCxneD2TlTlFhTxvvk8TUoFl6dbeKPdxf/aXFxiNqD8RN9S84lZcWqFGrXi3M5Y8MYTLHWmvPkvdVjQKk7vet8cjvLGQYuWL2Wnkab+fMgMBoWCG7PszLSbebPdxUqXjw2eAJu9AWbYzVyTacN8npPGZGRkZGTOjs/HFU1G5hPodDry8/Opr6+nsrKSsWPHApDmmE6woRancwUZGZchCL0jEgmCwONP/Zm16zfTvW03yqYW1B2dmIQgykgQSYJAVwAa4ySRkIgT1WsonTWb4q9+AVF7+jYTFwL6LdvJ9afRbGxhZev2PtnH9tpt/Hb+RsIxkXSTgiKHEYtOg0WvOyIr3ILdaMGiM6K9gDryj6x5iTgRNAk1X2tQoikrRTt4MADBD1egUlkJF8XY0vYOPu9UNBoNI0dmEI29R3NzKltLpU4jO+s6rNbje4kOHjyYtrY2urq6WLNmDbNmzbooZi1ASjT3fdxErD2AZU4RCktqsJ3wRglu6yTWGcI0JfeENiEGQylFhV+ltu4ZXK51KJRGcrJvPGWxY//+/SQlCfWE8UR27mSKwchTTz3FN77xjRN6xkuxGOF9+1Ji+Y4dJP2H/VYFtRrtoEEpz/IhgxH1qam/sVjsuG31Jnv27KG1tRWAy+YNRiGKmIwDiVcHAFDlHJvVdqRonvBF8cyvQz88HW25jcDWDpQWNeoCsyymy8jInBH9+vXD6/VSVVXFmjVrmD17NjbbqSUJGAwpn3OpzItlYFGfitgKkwnHfff2WfufNRJBJaGCENREEPbsJybsJ7Z2Md53/wQODYosB6qcPLT5pehy+qN3lKHSWM9oVtiZIIgC1qtKidR4CO3qIuGNEt7lpKjDgPedGvT909ANTlmjSJIE8eRxs9tPh0sdFhY5vbRHY3zs8jEjzdwbh9IrtEaifNTpYZXLT0ySAFje7eP23DRGmk9tpmhCknihsZO4JDHMpGey7eLKlO8N0tRK7s1PZ67DzKtt3ezwhVjk9PKxy8cV6dYzuhkhIyMjI3NhcOEoSDIy55jy8nLq6+upq6tjxIgRqNVqrJZRtChfJRZz4fXuwGIZ3mv7EwSBCeMugXGXHPV6wh8g1tRIcOsBwjuriDY1knB2Qgz4aD4N6zdi+9JNmGaPQ+ylYoTnilhHB7e3xPllBUTo4N1da7lq8Phea39j5UaeWriNWEKkNF3Dw9dej0Gr67X2+5LOkI89kRo0Gh1za3UYJQWWa65BEAQiVVWEd+5Co82ifbgThdiIzdZFSWkMr3c+AKJCS2bG5TgcMxDFE2cliaLIhAkT+Oijj3A6nezcuZPhw4efo6M8c6SkhG9lE9F6L4gCyVAcpV2LcXwOynQdgbWtxJr9uN+rwTw9H2Xa8T93s3kI+fl30tjwN7o6F6NUmsjMmHfS/UejUSorKwEYduut+N55F9rasHR28s9//pO77767Z91kOEx41y5C27YR2rnrKO9bUa9HN2wouuHD0Q4YcN48bw9lmwNccklqurzRMIBY60HhPPvTB8fhKjfJQAz/6hYS7gihPU6QwHadHoX58+fjKyMjc/YIgsCoUaPw+Xy0t7ezYsUK5s6di0538uu4Xl8Egkg87iYWc6JWXxwe0J91pKRE59alhMerUWdkYVVUEGtuIt7lJBkOITUGSDYGiFBPhNV4AEmvhHQtiqx01Ln5aPNK0RUMQG8vQa3O6BNBXVCKaCtsaMqsRBt9BLZ3QEfqJrEUP1ygO+mP4XqjMmUPY1GjsGpQWjQo0/Uo7afuY61ViFydYeWfLU7e6nAx0WZEc5779JWBMB92etjsTc2ABSjRafDEEzhjcZ6qa2eUWc9tOY6TZo+/3+mmJhRBrxC5O8/xuZ6NUaDT8L3ibPb4Q7zS6qQulMrEX9Lt5fpMG5NtJhSf4/dHRkZG5mJEFs5lPrekp6djsVjweDzU1NTQv39/RFGN3TaJzs4FdHR8iNk8rM87fwqjAUX//mj794cvpl6Le/z4FqzG/cbbJLqddD3zJ8K71mG76SbUebl9Gk9vYpwyhalLlvKHiI2gxsXTGz7oNeF81Z7VPLN0L4mkSP8sHT+6+np0mosnM/+p3e8RS4Sxx43c1SKgHTIYTVkZkiThfustANSjx+MUtqCmlrS0TYRDGhBE0tKmkpV5JUrlqdmuGI1Gxo4dy6pVq9izZw9ZWVlkZWX15eGdFVJSwr+yiWhdSjQ3T89HnXs4e0lbZkNp0+Jb3kTCF8X9YS3GcdnHeJsewm4bTyLup6XlNdpa30KpMJGWNvlTY6iqqiIWi2E2m8krLqb0+uvY9OxzTDMYueeee/jPX/7C/82cyXi7HammBuKJnm0VFgu6EcPRDR+OpqzsgvC9XbhwIQA6nUBubkro1kaKCUa7ETSKE954OIR+eDokJUI7uwjtdgKgypRFcxkZmbNDoVAwadIkFi5ciM/n4+OPP2bmzJkoT3LeFEUNOl0BoWAdgUA1SmwIKvFzLdhdCISru+lKLAKdgoxb7iQ771oApGiUaFsb4cZKQg37iDTWEG1pIuHsJhEKQ72fZL2fMLWE+Rg3IBlV4NCizM44KKiXoSscgM5ajEad0SuzQgVRQFNoRszRURfdQ8nYfDSWw9fDhDcKkCpgGogRawkc3BBMk/PQlJx6PaTp9lSR0K5onIVdXq7MsJ51/KeLJEls9QZ5v9NDZTDc8/pwk5556Rb6G7REkhLvdLj5qMvNZm+QXf5Grsu0MSfNcozFDEBDKMLb7W4AbstJw646/32eC4GBRh0/K8tlnSfAa23ddEXj/LWpi+XdPn5aKls9ycjIyFxMyFc2mc8tgiBQXl7Opk2bqKyspF+/fgiCQHr6LJzO5QSDtbhca7HbJ5zz2JQWI7Yb52K+bBJdf32D8NbVRCsraX/8cQyTJ2GaOw9VmvWcx3W6qDIz0fTrxyRnLYty3XRJLfgjEYxnKXAv2bGcFz6uIpkUGJpn4PtXXo+mj7yh+4I1ddtY3rAYQZD4QncWSqETy9XXABDeuZNodQ2CSkVtXh5hVwSTqQ2NRoPZPIzsnBvQak5f9C4oKKCsrIyqqirWrl3LZZddhlZ76tlS5wopKeFf1Uyk9gjRPP/YGwTKNB2WK0pSAnuTn0itB02Z9YQDkfT02cTjfjo6PqSp+Z8olHqsllHHXTeRSLB//34g5ScvCAIj7rmH+tdfh45O/p8jnZLGRjr//jIfKBRkZ2eRO3gIJfMuQz98BOrivrUNOF0ikQgrVqwAYMyYHEwmI1ptLnSmpp6rsg0nLaonCEKquJpGQWBTOwCaMmufxi0jI/P5QKPRMHXqVBYuXIjT6WT9+vVMmDDhpOdRg6GMYLCWrvXrSLQbsV1bLt/MO88ELftJWL2oVTYysuf2vC6o1WgKCtAUFGCZOLPn9WQkQqylhVBjJaHGfUSaaog1N5NwuUgEIuD3kajzEaKaEMtxAZJJBRk6lFmZqPMK0OaVoc8fgM5SjFp95tnOCZWEKsuA4oj+pDrXiP2L/Ul4IqmHO0KsK0S8PYh/bQtKh+6Uv3MqUeD6TBvPN3byfqebGXYThnPkfR1NJlnj9vNhp4fWSMoeTikITLAamZduIVd7+Bi0CoGbsu1MsBl5qamLymCYV1q7WeXyc1eug3LD4b5jPCnxQlPKomWkWc/E4xQ+/TwjCALjrUYuMRtY4vTydoeL4Sb9BdVHlJGRkZE5ObJwLvO5pri4mG3btvVME87KykKlspKZeQWtrW/Q2voGFstwFAr9eYlPYTKQ+e3biXfNw/3mW4S2bMG3cBnuN5Zivnwe9luvRLzAK7Ybp0zmKy8eYHm2joQU4g9blvPQ+Lkn3/AEfLhlIS+tbgBJ4JIiM/9v3nWoLrKiOz9e/WeCSTeZxkLu+OpPiNfVoc7LRUom8bz9DgDC2DHUOZ2AlYEDfoLFYkavLz6r/Y4cOZKOjg68Xi/r1q1j6tSpF1TnXUpK+Fc3E6nxgChgmpZ3XNH8EKJGgWlmAeE93WhKLSc9lqysa4jHvXR3r6Kh/i8oSgyYjP2PWa+uro5QKIROp6OoqAgAVUYGk26/g/0ffoCpsRGfz09TLMpWb4jtLc20rl9P0ZLF3Hnnndxxxx09210IrF27lmAwCMCcOf0REDCaBpB0J0AUUJ/EpuVIdIMdiGY18c4QmhJrH0UsIyPzecNsNjNp0iSWLVtGfX09ZrOZIUOGfOo2Bn0pXSwmJDZgkyDWHpCF8/OIJCXo6P4ApVVDVtYVp9R3FjUaNMXFaIqLsTKn5/VkKES0tZVw435C9fuINNUSa2kh7nGR9EfA5yVR7SVEJSGWpAR1ixrS9ShzstDkFKDNL8NQOASTbTCCcOb9RFGjQMzQo8pIHY+UlPAurEdh1SDqT28oPcFq5INOD03hKO93ergp237GcZ0KgXiCxd1eFnV58RycGadXiMywm5njMGP7lOzwfK2ah0uz+djl57+tThrDUX5W3cJ0u4mbsuwYlAre73RTH4piVIjclfv5tmj5NFSiwKXpFibbjSjl90hGRkbmokMWzmU+16hUKoqLi6msrKSysrLHvsLhmEV39yoikXba2t8jN+em8xqn0uHAcd+9hA8coPP3LxHvbMTzxpv4l32M/fabMc4cc8H6n+uGDcNoNFIe0LHP0sT86jMXzt9c9wGvbGgDBCaW2fjm3GtQXGSFdn6x7EW6kx2AwDf7X4NCoUBRWgpAcMMGYi0tiHodB+x2cLspKioiO3tYr+xbqVQyceJEFixYQEtLC/v376d//2OF4/NGQiLhj6WmQE/NQ1Nw8uJZgiCgG5R21GuBze2osgxH2bscWjcv70skEgE8nq3U1T1LWen30OkKetaRJIk9e/YA0L9//6MKqTpu+SJqq4WReXkcQGDXm2+w7pVXcMfjQEpwf+SRR3jkkUeYPn06d911F9dffz16/fm58XaII/3NBw+xAmAyDsQ4NhvDyAzg9AZxmgLzKX02MjIyMqdDVlYWl1xyCRs3bmTnzp2YzWYKCwtPuL7BUAZAXNtFUogQaw+e0LJLpm+R4km6PWuJRjpQKA04HDNPvtGnIOp0aEtK0JaUYOWynteTgQDRlmZCjfsJNRwg0lybylD3eUl6I+Bxk6hyE2QfQRbSrRAQh+Ziv/xm0vtf8ak1YU4VQRQwzy5AOIP+pygI3Jhl53d1bSx0epjtMPeJtUlnNMbCLi/Lur1EkikHc7tKyaUOM1PtZvSnGLsgCEy1mxhp1vPf1m4+dvlY1u1jszfIHIeZtztcANye68AqW7ScFIPi4kr0kZGRkZFJcXEpTjIyfUB5eTkATU1NPVmZoqgkJ/dmALq6lhIOt5y3+I5EW1FB7h8fw37HbYgGI4nuLjp//wzNDzxGaE/N+Q7vuAhKJYaJE7irOU4ykaQ7UMl/Ni89rTYkSeKVlW8fFM1h5oB07r/04hPNO3xO3mpKHXv/WC7zSsb1LJNiMTzvvQdAYsxYWt1uRFE8acbd6WKz2Rg5ciQA27Zto7u7u1fbPxsElYhlViHm2YVoCs9MmI3Uewnt7MK7uJ7QXuex+xAUFBR8GYOxH8lEmJqa3xOJtPcsb2pqwufzoVKpKCsrO2pbVUYGaXfeiXnWLC6ZNZPnnnuO1tZWXnnlFebMmXNUptWyZcu4/fbbycrK4t5772XNmjVIksT54JBwnpamIDNThyAoMBgqABBUCgTVxfU7kpGR+exSXl5Ov379AFi3bh1O57Hn8UOoVFZU6jQEjUhE2Uy8I3iuwpT5BL41DTRteYVkLEFG+mUoFH1jBScaDGjLK7DNuJKcO79D8UPPUPHcW1T86R1Kf/Ii2Xd/H+usq9EPGI7KnIGQVJDc2kTX40+y9+c30Lzhr8Tj/rOO40jRXEpKxN3hT1n7aIabdJTrtUSTEm+3u846liOpD0V4rqGD7+5vZH6Xh0hSIl+r5v/y03myXz6XpVtPWTQ/EpNSwb356fyoJJscjQpvPMH/2lwkJBhtMTDOcuoz12RkZGRkZC425NGyzOceq9VKeno6kiRRVVXV87rZNBizZThISZqb/3PeRK9PIioV2G6cS/5ffotx1mxQKIhWHaDl+z+l/Ym/kPCf/YCgtzFOnsyYwcPQJjNAghe2Ljr5RgeRJIl/fvw2b25NDZ7nDc3h/2ZdecFm2H8a98//LTHCqBJqfrgxQvvjv0BKJgHwr1xJwtmNaLGwx5gagJSVlWEynVoB0NOhvLyc3NxckskkixYtYu3atXR2dp6X77gkSUSbfD3/CyoRdc6Ze2Sqc41o+9lAgsD6NsLV7mPWEUU1xUVfR6vLJx73UV3zFLGY+6hs84qKClSn4Juv1Wq5+eabWbBgAfX19Tz22GNHCe4+n48XX3yRiRMnMmDAAH71q1/R0nLubsR1d3ezadMmAGbOrECr0WIwlCFyYVs8ycjIfH4ZMWIEOTk5JBIJPv74456khuNhMJQhqBWE1Y0kvFGSwdg5jFQGINYVoqvlY6JRJ0rRhMMx7ZzHoDAa0fbrj33WNeTe/QOKH36O8j+9RfEjz2McORFBUMH+Lty//yv7H7qBhiVPEol0nvV+k5E43oX1eD6sJeGPntI2giD0WLSscPloi5zdd1aSJHb5gvy6ppUfVzaz1u0nKaWKU36vOIvHy3OZZDMdt7Dn6TLAqOPx8jxuzLKjFgVsKgV3yhYtMjIyMjKfcS4+5UlGpg84lHVeXV1NIpHoeT0n5yYEUYXfvx+PZ/P5Cu+4KC1GMh+4g7w//BLt4KEgSYR3baDtJz/Bt2QJ0kH7iAsBZVoaaffczbSCUSBAV6KO77/9O95c/z4rdq1gU+V69jTsoL79AJ2eZoJhF8lkhGQywabuKj7a4wXgupEF3DXtsouygz5//2r2hSsBuKrBhF0SMU6ZjCCKJEMhvB9+BEBkzGi6vV6USiWDBg3qk1gEQWDcuHGkpaWRSCSora1l0aJFfPTRR+zfv59o9NQGf2dLMhwnsKYF7+IGgjvOfgALIChFDOOy0Q5MDUr9q1uOEuYPoVDoKCn+NmpNOrGok5bW1+no6MDpdCKKIhUVFae97/z8fB566CEOHDjAxx9/zF133YXBcDgLa//+/fzwhz8kPz+fefPm8frrrxOJRM78YE+BpUuX9twQmTo15ZFvNA3E/V4N7vdriLv7dv8yMjIyp4soikyYMAGLxUIoFGLVqlUkD95k/iQGfSmCKBA1tQIQa5ezzs8lkiTh39iEW78KUa8kK/9KRPHsCsD3FoIgoCsfQOF3n6DsiX9jmToPUW2ARi++v75B5fduovadnxD01Z/5PpQKpEQSKZrEt6IJKXFqCQj9DFqGm/QkJfhf25nN/IsnJVa7fDxc1cyva9vY5Q8hAOOsRn5enssPS7IZ2geFKJWiwJUZVv44oIBfVeRjvsjqDMnIyMjIyJwushmZjAwpwUuj0RAKhWhubqagIOV5rFE7yEi/lPb292hpeQ2TaQgKxYUxIDiEpjiH3F9/H//6HfgXvUusqRn36//D88ESzPOuxjhj9AWTnf2zWTez8B9LSRLiw47VfNi+DkXChjZpw4QDY9KE6uBpSRSTqMQknoAOjQa+OLaU68ZOO78HcIbE43F+sfHvQBJH2MyXmkVUebkYp00DwLd4MUm/H0VGBtuUSohE6N+/Pzqdrs9i0mg0zJkzB6fTSWVlJQ0NDbjdbjZv3sy2bdsoLCykrKyMtLS0Xht0SUmJeFeIWLOfaLOfuDMEEiCAaOi9DGhBEDCMzkKKJIhUe/Ata8Q8t6insNchVCozhQX3UVn5OG73Jrqd2QCUlJSc1XsvCAKTJ09m8uTJ/PGPf+SNN97gpZdeYsWKFQAkk0k++ugjPvroI+x2OzfffDOlpaV9kvF/yKZFFKGsPHX8erGMiDuSet9Ps7CZjIyMzLlArVYzdepUFixYgMvlOmHWucGQSnyI6lqQSBBrD6IptpzLUD/XRBt8uDyrSZi86NJySEubcr5DOi7q7Fzy/u/HxG/8Ol0f/RvPsg+Jd7kJvrqYmg9XoJo0DIWhlEQidEqzzQ4hKARMU/Nwv1tNvDNEcGsHhksyT2nbL2TZ2O4Lst4T4IpQhCLdqY0vwokky7t9zO/y4IylkmTUosA0u4lLHRbS1edmRpns1y0jIyMj83lBHjHLyAAKhYKysjJ2795NZWVlj3AOkJFxKd2uNcSiTjo6PiQ7+9oz2oezexWtrf9DpbKi0xWi0xWg1xWi1eb3ihhvHDsUw+jBBNasxf3mW0SqGul86mk871Xg+Nod6PqduMDWuSLYvIprTLA4YCUgBEgSI6noIig4CVFDuwSKuAVlzIwuYcWSsCEicsf4Cq4cfWEOxk6Ff9euIaD0IURF7t+jRCmI2L54M4JSScLnw7d4CQD+S0bh8/vRaDTnpGinIAg4HA4cDgejRo2itraWqqoqPB4PNTU11NTUYLVaKSsro6ioCLX67Ipqud+tJvGJDGeFTYN+eMYZe5qfCEEQME7IRYokiDb58S5pwHZdGaLm6MueXl+E3lCK270Pl2slMIwBAwb0WhxGo5E77riDO+64g+rqal5++WVefvllGhoagJSVynPPPQfA3//+d+6++25uvfVW0tPTe2X/h4Tz4mI9aWlGFEoDSmcaEdpQpusR1fLAV0ZG5sLEaDQyZcoUFi5ciN/vJxQ6VtTUanMQFVqSWj8Uh1Fln5rXsiRJeDybUalsGAylfRH+Zx4pkcS/OZVtrjCrycq5sleKb/YlSpuNrFu+QcY1d+Nc8gauBW8R624n9tEmshNrqFz1DopMG6rsbNQ5hWjzytHn9EOnz0OhMB43kUBhVGOcmItvWSOhXV2osvSo805us1eg0zDeamSN289rbd18vzj7U9d3x+IsdHpZ4vQSTKRmYJiVCmanmZmVZsYoZ37LyMjIyMj0CbJwLiNzkEPCeXt7Ox6PB4sllbEkimpyc26iru45OjsXYLdPRKPJOOV2JUmio3M+ba1vApCIBwiHmnGxJrWCIKDRZKLTFRx8FKLXFaBQ6D+l1eMjiCLGSRPRDR1G14v/w798Wcr//Ls/xjBxMo77bkJpP3+ZWO1V/2NeopJbxWGU3v4v5leuZmH1Orx6Ba3hFvwRN5Gon1jESQzwSqBO6tGZR5y3mM+W7kiAf+5/A61GxwSvgxEhP4bx49Ac9MH2fvgRUiSCsiCf3QftdQYNGnTWIvXpolar6devHxUVFXR1dR2Vhb5p06ZjstBPhJRIEusIEWv2Ee8KY55b2DPQVDp0JIMxVDlG1LlGVDlGFL2Yaf5JBIWAaVo+3kX1aMqsx4jmh0h3zKKpaSNqjZf09Mv6xFceoLS0lJ/97Gc88sgjLF26lJdeeok333yTcDhVVGznzp088MADfO973+PKK6/krrvu4tJLLz2t7Lcjqa6upra2FoBLLx2EUqHEaBxArDaVuanOkYt5ycjIXNikp6djtVppb2+nq6sLs/nom6yCIGLQl+JL7EYo9aNxnPwmrCRJtLb+j87OhSCIFBd9A7O5dwtxfx4I7+vGHVtLQhfAYM/Hbp90vkM6ZUS9nvQrb8Nx6c24Vy+k691/EamvRmgLI7W1Et3eSpQt+AEUApJdg5BuRJWVjTq3AGP5KNJK5yAIqRmdmkIzsf42wvtc+FY1Y7uqFFF/8mv39Zk2NngC7PSF2OMPMdB47Gy3lnCUD7s8rHb5iR+cmZalUXGZw8IkmxH1BTKrVEZGRkZG5rOKLJzLyBzEYDCQm5tLc3MzlZWVXHLJJT3LzObhmEyD8Pl209zyKiXF3zylNlODs9fp7Exlfaanz8FgKCcUqicYaiAUqice8xAJtxEJt+F2bejZVq1Jx+GYSZp9KqJ4ej9VhdlI5v+7E+s1s+n6878I795JYOXHBDdtxHLlFdhunoeoObfFAYPBWmJpUegQ0WxL0u17lsllZcwqvxZNcRGCWk21r5NXd3zE+sZttMe6CAt+okKQH214lgJ7NhXpRec05t7gF9tfJxTzYUzo+fJ2P6Jej+W66wCId3biX/kxAN3DhhHyeDAYDD2e++cDQRBIT08nPT39qCx0r9dLdXU11dXV2Gy2nix0lUpFwhsl2uIn1uQj1h5Eih32ok10h1GmpQaChtFZGCfkIPRCgapTPh6liPnSok+1m1EoyggEBEQhRH5B33t+i6LIrFmzmDVrFm63m//85z/84Q9/4MCBA0DK2uett97irbfeIjMzky996Uvcddddp+15fyjbHGDsuBwAjIYBxFoDAKjOogirjIyMzLni0M3azs5OSkpKjlmuN5Ti8+0mEKjC4ZjxqW1JkkRb21sp0RxASlJf/zylpd9Bry/u9dg/q0iSRKjeiVu/GoVFQ2bWFYjixVd0WlCpsE27HMOE2Sx65RUm9ish2lpFpLmGaEsT8fZ2kpEgyc4IdIaJ7ekixk4C4oeE764kb8bh8YDhkiziHSHi3WF8q1uwzD75TM8MjYrpdhOLnF5ebevmkdKcnv7KgUCYDzrdbPEetikq02u4PN3KSLMe8SKs9yMjIyMjI3MxIgvnMjJHUFFRQXNzM7W1tQwbNqwn01MQBHJyb+bA/kfweXfg9e7AbB76qW0lk3Gamv6By7UWgOycG8hInwuAxTK8Z71YzEMo1NDzCIbqiUWdRCOdtDT/F6dzOTnZN55RNpSmJJfc3/wA36ptdP/t38TbW3G//jqRfZuw3XozusF9U3zyeHR2LkJQqbAahqOIQmTvPiJ796UWKhVkPPAApaWl/Gji7UjSbQiCwOqqzdy/+gkiQpC75v+Md6/9HWlG6zmL+Wz53/aFLDnwASqNhq8Mvos05160AwegOJjR7Hn/A4gnUJaXs+egf+vQoUNRXCC+kYcsY/r160dnZydVVVU0NDTgcrnYuHEjW7duJUftILvTgEV1WIQVdUpUuUbUOUZE0+HMeVFzfo7rSNE8GY4T2NSOYUxWj03J/v1VRCLlWC37iEU3A3PPWWxWq5V7772X3NxcioqK+Pe//80///lP2traAGhvb+fJJ5/kySefZPTo0dx1113cfPPNmM1mYrEYsViMaDR61N9Dz9955x0AtFqBgoLU56CLlxCKeBDUYs8NDRkZGZkLmUPWVU6n87jLDfrUDK5AoJq4K4wUSx5T0+IQ7e3v0tGRKsadk3MjXt8u/L491NT+kbKyH6DVZPXBEXz2EASB2KgahJokWms2NtuE8x3SWSEIAnGrFcOocVhVk3telySJhNNJpLmeUNMBwk3VhGsPEGmowv2v1zCWjcZaMC7VhlLENDUP38rmU/Y5B7g6w8rHLh81wQgbPQFEQeCDTjdVwcM38kea9VyebqXCoO29g5aRkZGRkZE5JWThXEbmCLKysjAajfj9furq6o7K/NVqskhPn01Hx3yam1/BaOx/Qi/HZDJCXf0L+Lw7QBDJz7sdu33icddVqSyoVEOOEsbjcT8ez2ba2t4hEm6jtvaPmEyDycn5Alptzmkfl2nScAzjhuB+fQG+RR+RcHXR9cwzaAcNwnzVNWgK8067zdMhGu3G7dkMQMGlD6IcLRKpqiJaXU2ksoqE14sq+7C3o+ftdwjt2E5FcQk3uSv4r30fAdzcsehx3r76VyjFC0NY/jTi8ThP7fgvEgnMURPXDpgMAyb3FICMNjUT3JCaYdA+cAAxlwur1Uph4fn3oj8edqWZEeYKys3ptFn81HU34fP5qAs0U+UKYjenstBLhlagSTf0WkHR3kSSJLzLGom3B0n6Y5hnFRBNxKiuriaZLMdkaiUYrCUQqMFgODarsa8ZOHAgv/nNb/jFL37B/Pnzeemll3jvvfeIxWIAbNy4kY0bN/K1r33ttNodMjQNi9mMWpOB0JGqp6DKNCAoLrzPSEZGRuaTOBwOAFwuF7FY7Bj7Kr2+BASRsKedrk1b0aZlYr382HN4e/v7tLe/D6RE8/T02djtk6mu+S2hYD01Nb+nvOxBVCprnx/TxU4iEaazawEKvYqszCtOe2bkxYIgCCgdDpQOB4ZhowCQ4nGqf3o3kdoqWp79GYZH/41KawNAYdFgubz4tPpAFpWSSx0W3ulw80xDB4fKhCsFgYk2I/McFnK0F7Z3vIyMjIyMzGcZ2RRNRuYIBEHoEcv37t1LIpE4anlGxhUoVRai0a7D03w/QSIRpKbm9/i8OxBEFUVFXzuhaH4ilEojaWlT6d//MdIz5iIICny+Xew/8ChNzf8hHvef9rGJSgX2L84j/5lfY5o9C5QKQtt30vSNH9H2q78Q7/acdpunSlfXUpCSGI390OsLUefnY5o+nbQvf5nsX/2S7F88jqg/nB0Wqaok3tpGcPUqpi/fyV2JYShVWlx08aNN/+qzOHuT7y/6AwHcCJKCxyZ9pef1Q4Mpz9tvgyShHDKY/V4vAMOGDUO8QLwqk5EEkToPvtXNuF4/gPvdagKb2hE6ohQrs7jiiiuYOXMmReXFaPPM+M1xtnXs493lH7Jp0yZcLtf5PoRjEAQB45gsBJVIrC2A9+NGNmzYQCKRwGrNJisrVYC2y7n0vMapVCq54ooreOONN2hpaeEPf/gDw4cPP+P2rr56JIIgYDIORGnXoik2o+7lYqwyMjIyfYVer0epVCJJ0nGzzhUKDTptHqJGQVjVSLwzRKT26D5NR8d82tpSs3Cys68nPX32wW21lBR/C7UmnVjUSU3tH0gkgsfsQ+YwkToPnR2LScQDaDSZ2GzjzndI5xRBqaTgW79C1BuRGt3U/uNHPUkRcPQst1hnkGQ4ftI2L0u3YFSISIBeIXJlhpXf9c/ny3npsmguIyMjIyNznvlspgfIyJwFZWVl7NmzB7/fT3V1NRUVFT3LFAoNOdk30tDwF9o7PsRmG4da7ehZHou5qan9PeFQM6JCR3HRNzAaK463m1NCodCTk30DafYptLb+D49nK86uZbhc68jKvIq0tGmnneUj6vVYr78ew6TJdD73D2Kt2wisXJHyP7/qoP+5uvdODYlEBGd3ysc7PX3OMcsFQUBpsx31muMrXyFaU4N/+w54602uWtuA9guX8XffMlY3r+CJnTa+N+SqXouxtznQWcfyrlQ2+bhOG+WLN5O4qRSFMWVnEtq5i/CuXSCKNJaUknB1k56eTk7O6c8m6AsS/iiuNyrh8DgQQSGgzDKgzjWizjMhCAKZmZlkZmYSDoepqamhqqoKv99PZWUllZWVpKWlUVZWRmFhIUrlhXG5UabpMM0owLOwji27ttModqF26BkxYgRm8wBcrrV43JuIZX8Bler8FdI9hMPh4P777+f+++9n27ZtvPTSS2zYsAFRFFGr1ahUKlQq1XGfq9VqMjMzuXyeB/BgMg1EbTGhzuub4qcyMjIyfYVWm7Ko6OzsJCvrWDsVg6GMUKiBZIELKsG/qhnRpEbl0NHZuYjW1jcAyMq6hoyMS4/aVqk0UVL8AFVVvyIcaqK27jlKir91UXp29zXR1gDuFVW0ZL6JmCWSmXklgnDhzwLsbdQZOWR/+Xs0P/0oseXbaRnwF3In33fUOuFKF/41LahyjJhnFXxqFrpBoeCHJdk0hKOMMhvQKS6MJAoZGRkZGRkZWTiXkTkGlUrFkCFD2LRpE7t27aK4uPioacFW62ic3SsI+A/Q0vI6RUVfBSAS6aCm5imi0S6UKjMlxd9Gp8vvlZg0mgyKir6Gz7+PlpZXCYeaaGl5NeV/nnMjJtOQ07bGUGVmkPPod/Gt3EL33/5DvKMN96uv4Vu6nLQ7bsE0/ZKTN3IKdLtWkUyE0GgyMZlOzaddYTKhGzYM5cCB+CsPkNHRwaVvrafjC5P4oPtj/rPzn3jbGvn57K/3Soy9zf9b/HuSxNDFdXy7UiBs3odwMJNcikZxv/YqAIrx46hydQMwfPjwc25vkgzHiTb5iTX7EdQixvEp4V40qBD1KgSViDrXiCrXiCpDj6A8/kBOq9UycOBABgwYQHt7O1VVVTQ2NuJ0OnE6nWzZsoXi4mLKysqwWq3n8AiPjzrbQEO+n/q2VgBGVPTrEWL0hlKCgWqczhVkZV1YN2eGDx/OH/7wh9PaJhp1snfvgyCIGI39+igyGRkZmb7lSOH8eOgNZdC1lJijA3XISLTJj29pA7EJ9bQ5XwcgM/NKMjMvP+72Gk06JSXfoqr6CQL+/TQ0/o3CgnsRBFnAPISUlAhubMOrW4eki6HVFWO1jj7fYZ03rONm49+9Hs+SD3G9/C9MpaMx54zoWa5M0yGIArFmP5EaD9pS66e2V6DTUKDT9HHUMjIyMjIyMqeL3BuUkTkOpaWlGI1GwuEw+/btO2qZIAjk5nwRBBGPZws+3x5CoQaqqn5NNNqFWu2grPTBXhPNj8Rk7E9F+cPk5d2OUmkiEmmntvbpVJZ7uOXM2pw8kvwXfoX1li8i6A0kOjvo+O3vaf7uL4i1tCDFYkdNQT0dJClJV+diAByOmWckDHtHjUI3ahQkEtyxoJIcspESCd5vW8YL618/o7j6kr9vfoemeANIcFuVHj0i1utv6LGi8S5YQLyzC4XVSvXBDPPc3Nye4mfnimiLH9cblfhXNROp9RCp9SAlU5+zIAhYry7Fdk0ZhtFZqHOMJxTNj0QQBLKyspg0aRLXXHMNw4YNw2AwEIvFOHDgAB9++CELFy6kpqaGePzkU5f7iv3797O/rRqFXctgUylpLWrClSlrGYdjJgDO7hUkk+cvxt7C598LgF5fTLJTIu6OnPHvWUZGRuZ8cUg47+rqIplMHrPcoC8FIBxpQjcpHYVVgzu5nsa9LyNJEhkZl5GZeeWn7kOnK6Co6OsIggKPexPNLf+Vz5cHkZISwU3tRFzdeA0bUJg1ZGVe9bm/sZDzpe+hLihCCMZpeuYnxKLenmVKuxbdsFTfLri1Aykhf5dkZGRkZGQuRuSMcxmZ46BQKBg2bBirV69m7969lJeX9wzaAHS6PBxp0+jqWkpj0z9IJAIkE2G0ujxKir/dpxYPgiCSljYZq/US2js+oKtzMX7fHvYfeJQ0+xSysq5CqTw9KwZRpSTt1suxXD4F519ew79yBbGWatp+9nMAkqE48e4wgqgAhQJEBYIi9VxQKFBlGFHlOrDfdedRtite7zai0S4USgM224QzPWCsX/oSPqUK05w5/DvTwWWvfYvuZAd/PvA/8iyZzOs/5cza7mXiyQQvVqYKjxUEbVzpVKKpqEA/JpWRFWvvwLtgAQDCnNk0trUBKW/zc0m0xY9vSQNSQkJh1aAuMKHONcIR9zVE9dlNvdbpdAwaNIiBAwfS1tZGVVUVTU1NdHV10dXVdVQWusVy7ixRamtr2bw5Vah2xPhRlMQziLUFEPWpWSUW8wiUKgvxmAePZzM229hzFltf4PPtBsBkHIh/XSsJdwTT9Hw0sse5jIzMRYRarUaSJOLxOG63G7vd/onldlRqO7FoN+F4A/FRrTh3fQRRCZtiCllZ157SzXuTsT/5BffQ0PAXnF3LUCktJ8xS/7yQDMXxfdxErDWAx7AOwQY6Qz4Wy6jzHdp5R9RoyL//V9T++G6SdU7q//UQpXf9see7phuYRnhfN0l/jPD+bnQD085zxDIyMjIyMjKny+c7TUBG5lMoKCjAZrMRj8fZvXv3Mcuzsq5GqTQRizpJJsIYDOWUlX7vnPkiKxQ6crJvoF+/n2GxjAQpidO5nL37HqKzc9EZZcsqrSYyv3cPuU8+hmHCyCOWSJCUkOJxpEgEKRQk6feR9LhJdDuJdbQTOXAA3+LFR7XXeTDbPM0+BYXizKefCioVaXffhTovF41Kw3+vehwDZpLE+OnGP7Otee8Zt322xONxKjvrWdWwg1/seAtJE0enMvD93SIoFNi+eDOCICBJEu5X/wvxBJqBA9h9sPBscXHxObUvOVI0V+ebsF5ZgmFkJqpMQ59YxQiCQHZ2NpMnT+bqq69m6NChGAwGotEo+/fv54MPPmDr1q29vt/j0dzczLp16wDo168fgwYNQjcsHculxakbB4AoKnGkTQOgq2vJOYmrr5AkCf/BjHODqoKEOwICqLL0J9lSRkZG5sJCEATS0lKi44nsWg5lnbe1vUWL6xWUaVoy8udSMPi207q+2ayjycm56WBbb+PsXnWW0V+8xN1h3O9VE2sNkFSHCBbsQmFQk5V59Tm3l7tQ0eYUkHnXt0AQiSzZQtu6l3uWCUqxJ+s8tKMTKZY4X2HKyMjIyMjInCFyxrmMzAkQBIHhw4ezbNkyKisr6devH8aDxR3hYOHOnJtpaPwrFvMwCgq+jCiqz3mcKf/zr+L376e55b8H/c9fw+lcccb+59ryArTlX0eKRpHicZKRGFI4RjIWg1iCZDwB0VjqbyxOorsVz9tvEFy7DuvVVyOo1QSDtQQClQiCAodjRq8es7nVye/cA/mmZTtRIcRXl/6KN69+gmxzRq+03+V3sbezhu5YmLhGgzPspS3QxbbKjQQTIULJMFEpRowocaKAhKhQotOlvh9XdGZRGPNjmjsLVXY2AKEtWwjv2YugUhKcPJmOvXsRRZGhQ4f2SsyngpRI4l/d0iOam6blIZzDAlR6vZ7Bgwcfk4W+d+9e0tLSKCgo6LN9d3R0sGrVKiRJoqioiJEjR57wd5GWNpX29vcJBmsJBmvR64v7LK6+JBSqJxEPICq0KLsdRGhHmaZD1MiXfhkZmYsPh8NBR0cHXV1d9Ot3bM0Gg6EMt3sjwUANAOk5s8jNvaXnXC9J0in3h9IdM4nH3HR0zKep6Z+olGbM5nN3vb5QUBjUCGoFCbWL7tIPEJJJdPoizOZzO1PuQsc++Ur8uzbiW7mY7pf+jqnkEoyZgwHQltkI73aS8EYJ7XGiH9Y7fVUZGRkZGRmZc4M8epaR+RSys7PJysqira2NHTt2MGHC0XYjNtsYzOahKBTaE7Rw7jAa+1FR/jDdrjW0tb7Z439uNA0gJ/smdLrc025TUKsR1GrEkySoSskBeBcuJtrYhvu9Fdiun01n5yIgVUxVpbKewREdn2QkgvMvL5Lj8/HIkME8bNpOSPDxxfceZuHNz6JWHHtaC0SCVDkbqHI24kvGEAwmnBEvHQEnO6s3EUpGiCQjRKUYcaJIQioj6EgxXEIiGPWcMC4RBWatgyJNDreta0Vhs2Ged1kq5lAI92spP3b9zJmsqKsDYODAgRgMhl57b06GoBAxzyogtNuJcXz2ORXNj0QURXJycsjJyWHr1q3s3buX9evXY7fbj7o51Vu4XC5WrFhBIpEgJyeHsWPHHiOeJMNxwgdcqAvMKK0mrNYxuFxr6epaSkHBPb0e05mSTEYIh1tIJqNIUhJJiiNJiYN/j/7fHzgAgNHQj3hjGABVzrn7vsnIyMj0Jg6HA/iUjHNDWc9ze9rko0TzRCCGf2UThtFZKNN0p7S/rKzriMW9uLrXUFf/Z4oKv4LJNPgz7+stxZOgEBAEAUElwjgXHR3/IpmMoFSayMv9kpxtfhxy7/oR1dX7iLU00fjMjyn/6b9RKg0ICgH9yAz8a1oQztIGr6+QEhLReg+hvd0gCJgm56Iw9V4yUKSmloTHjTI9A2W6A1Fz+rNQpUSCWGsbscYGog2NRBvqiTU1I2q1qAryURcWoS4sQF1QgOIcWgDKyMjIyHz2kYVzGZmTMGzYMNra2qirq2PAgAHYjvDwBi4I0fwQgiCSZp+E1TKKjo4P6exchN+3lwP+R0mzTz0j//NT2q8oouk/isj+d/AtWIbhylG4PSkf6fT02b26L1GjIe3uu+h85lmG7GzgG2OG8UfVFkKaIPesfAqbyszeuu2EkxGiBzPCE8QOb69QoNOl3gMJiWD8CDH8qHGgiFLQkG7Iw6AyYNGY6UrUY1LqsWtMZBhs5JszKbLl0C+9GJv+sGd0cnyIeFdXz8DA8/77JDwelOnp1OXkEKqqwmg0MnDgwF59b06EFEumBr+A0qbFNOn0b6L0FUOHDqWjowOn08maNWuYNWsWoth7ooTP52PZsmXEYjHS09OZNGkSCsWxA1f/ulaidV6SwRjGcTk4HNNxudbidm8kO/uGc2bBdDzicT9e7w483q34fLuRkrGTb3QERuNAYq0BANQ5vX9jQkZGRuZckJaWhiAIBINBAoHAMTeetdp80tNnI4oaMjOvOkrcDW7pINYWxLu0EevlxT11LT4NQRDIy72NeNyHz7uT2tqnUSpNmMxDMJuHYjIOPisbuguRhCeCd1kj2jIr2kE22trepqNjPgB6QylFhV/p1WSIzxIKrZaCb/2SmofvJVndQf1/f0rJrU8gCALqQjO2HONZ14/pbaRYgvABN6E9TpKBw30L34omLJcX98oNkkhVFR1P/g6OKLSrsFhQZmSgTE9HmZF+8G8GSocDUadDiseJtbYSbWgg1pASymNNTUixY/s/iUiExE4P4Z27jmpfXVSIqqAAdUEh6sICFGa5touMjIyMzJkhC+cyMichLS2NwsJC6uvr2bZtG9OnTz/fIZ0UhUJHdvb12O1TaG39Hx7PFpzO5bjc68nKvJK0tOmIYu/+/K3XzsL7/vvEmhtoW/cKWJIYjP3Q6XrffkM7YAD2275E999fZtaGBoSrLucvkY+pce0FJALJ42eGK1CjEfTkW8owqoxYNCa6muuxqHSk621kGx0U23IoSysg15yBUnn0eyRFo8RdLpJeLwmfn6THS6KpiaRvD10+P8bJk9AOGICo06HOzwcg2tSEf9ny1P7nXcb+6moALrnkkmPa7wuiLX58HzdhnpaPKuvCyzZWKBRMmDCB+fPn09XVxc6dO3utWGowGGTp0qX/v737Do/rus99/93TZzCYQa8EAbCCvYlisUSqsKjLsRMplo9sOY7rSRQ5LjeKb67lk7jEx/a5iftNXBLbku3YqpFMiZJYRJGUxN7ATgAk0dvMYDCYuu8fEEeAWASCAAEQ70cPHpAze69ZG1rc2PPO2r9FT08PWVlZrFy58qI/c3dVDrGaINHjnXgWFuLxVOLJmER3+CRt7ZspKrx7SPo0ULFYO8HgHgKB3b0zx81U+jmbzYfV6saw2DCwYhhWDMOGYVgwDPvb320YhhWbzYffWEQwcgbDbsGWr/rmIjI22Ww2srOzaW9vp6Wl5bzg3DAMSkruu+C+GUuKSLR2kwzECG44jX9tBYbtvT+ktVhslE/8FPX1T9AZ2EUiEaKjfSsd7VsxDCtebxU+3zx8vrk4HGN74cdobZCuLWcx4ym6Dp/mrPMXhLuPAJCXv4riog8O+bXjtcZVNpmCB/8nTT/9Lj3rttE847cULupd62a0zTaPHGyle28LZqz3+sLisuKqyiHe1E3G4qIhCc1T0Sjt//GfYJpYc3Iwo1FS4TDJQIBkIED02LHz9rFkZpKKdEPi/HrwhsuFo6ysd4b5xHIcZRNIRSLEaut6Z6DX1RFvaCQZCBDZu4/I3n3pfa3Z2TgmluEoL8d+LkzPHPrJRCIicu3R1Y/IAMydO5e6ujoaGhpobGykqKhopLs0IE5nfp/657+lJ3Ka+vrf0dq2kZKS+/Blzh2y223t+dm4Z8+je+8OOl95AT5QMuSzzfvKWLqUZEcHgWee5dbn3sT6odt5y96J3+ml9WwN2Q4vxd48ynwFTM0vZ0puGU77+TPDUj09JBobSYZCpEIhkicCpPZuJRDqIhUK4r35Ztxz5gDQc+QIrT/44UX75JxUiWvGjPTfTdOk44knIJXCvXABu0IhTNOkrKyMkpKSof+hvEusIZxeCDRyuH1UBucAmZmZLF68mK1bt3Lw4EEKCwuv+N9YNBpl48aNhMNhvF4vN998Mw7HxW87thV6sGY7SXZEiR7rwD0rj7zcW6kLn6StbSMF+bcPe2DQE22gveMAgcBuIt01/Z5zuUvx+xbg9y/A5Sq7rH+3kQOtANgLPRhW3V4vImNXfn5+OjivqKgY8H4WhxXfrRPpfP4UiZYIXdvq8d5QOqBzqdXqpKzsIUpL/wfh7uMEg3sJBvcSi7YQCh0kFDrI2bOP43KX4such883D49naGbrXg1myqR7VxORA20AJIpaaSt5lkR3EIvFyYSyj5KdtXiEezl25N7yAcIH36Rr+2u0/ttP8JYvJCNvGtB7XRg/00WiLYJn/gjXOjfBjKWw+h24Z+XhnOS/4IdJsbNd2AvcGPbLD/4DTz1FoqUFa3Y2Rf/3l7F4PCS7wiRbW0i0tBBvbibR3PvnREsLqbevxQEsHjf2som9YffEidgnTsRWUHDBf1fOyZPTf05Fo8RPnyZWV5cO1BONTSQ7Ooh0dJwfppf3huj2iRNxlJdjHYaSgSIiMrYpOBcZgMzMTKZMmcKxY8fYu3cvhYWFY+YNEZyrf/5/v13//Cli0WZqTn3/7frn9+F2TxiS1/HddSuhA69iVLdhT87Clzm8C2ll3nYbifZ2wq9t4eb/eo37/68v4ZgwgeS0MPHTdb1heHOI5IkDhIPbCIW7SAZD+NauwT1/PgDRY8dp/cEPLvoazhkz0sG5NTMTi8eNxZuJJdOLNdPX+93nw+L14pw6td++4a1biZ04ieF00nnddbQePozNZmPhwoXD9jM5J9YQJvRybe9CoBO8ZN44esqzXEhFRQWNjY2cPHmSbdu2cfvtt+NyDa4MUjweZ9OmTXR2duJ2u7nllltwuy9d09YwDNwzcunaWk+kuh3XjFz8/oXY7H4S8QCBwC6ys68fVH8uxjRNIpEa2trfwul8luPHn8VyrnauYeDxTMLvX4DftwCnc/BvsF1VOViznAOaXSkiMprl5+dz5MiRi9Y5vxSrz0nmygkE19cSPRHAmuXEMyd/wPtbLDYyvVVkeqsoKb6PaLSBYHAfweBewt0n6ImcpSdylubmF9IlXfy+eXi9s0a0pIuZSGHGkqRiKTBNbNnv/G6NHGojWhMk0dyNiUl0+hHaPOvBTOJ0FVFR/hlcruH/oP9aYhgGEz7xGMdP/g8SzQ3U/eBRpn7519hsrt5SOK/UgUHvmio5w1/u0TRN4o3d9BxsxTkpC+ek3tJzzmnZWHwOHGWZF31PE2/uJvhKHVafA9/NZVj9Ax/HPYcP07VxEwA5H3kQi6f3jjerNwOrNwPHBT74SnV3k2hrw3A6seXnD+q9lsXpxDllCs4p76x5kOrp6R+m19aSaG5+J0zfsye9rTU3J13epTewL8fqHZ0TT0RE5OpQcC4yQLNnz+bUqVO0tbVx+vRpJk4c+hIkw+ni9c//F7k5KygquveK65+7l8wiXtAD7SaeXTkYC4f3wwXDMMj+8z8n2dnZuzhQYSEAsZoaWr///YvuF29p4VyMavX7sGZnY/VlYvFmpr9bfJlYMzNxVFam93NUVFD63e8OqG/Jri4CTz4FgGftWrafPAnAnDlzhn1B0PNC85vLRmwh0MuxaNEiWltbCQaDvPHGG6xYseKy3zQFAgFee+01gsEgdrudm266acALjjon+QnvbCLVFSd2JoRzoo/c3JU0NT5La+vLQxKcp1IJwuGjBAK7CQb3EI93kjJTGJYAhlFCZuZM/P75+Hzzh6yuumGz4Jig25FFZOw7t0BoZ2cnsVjskncSXYijxEvGkmLC2xvo3tWMLcuFo+zyz4+GYeByleBylVBQcFvvWhSh/QSD+wiFDvQv6WKx4c2owuebi883D4cj57Jfb6BM0yS8vYFES4RUTwIzmsRM9qktneUk+/3vBIo9RztIdkYx7QmCszbTZfTOxvX7F1FW9tCoWsdnLLG63Uz8m69x6iufJnWkgaYNP6N09Wd7x1uFj1hNkO5dTfhWlQ9bH8yUSawmSORgK4m23gXCUz3JdHBucVhxTnyPut9GbwmXZGeUzudPknnjhAH9e0l1d9P+n78EwLtyRb+7MS/F4vHg8Ax9STmLy4Vz6tR+E1xSkQix06d7a6jX1hKrO02iqYlkWzuRtnYiu3ent7Xm5vQuPjpxYjpQtwzztbyIiIweCs5FBsjtdlNVVcWBAwfYu3cvEyZMGNJFDK+WdP3z3JU01P+eQGAnbW2b6Oh8g8LCu8jLvXXQJSm6uvaTWOjBtj6MuaWR1F+khv1nZFit5H3iE2C3p0NWa1YW9pJiLJk+rJnePt97w3D7hHdm2DsmTqTkG18f8n4FnnqaVDiMvbSUY1l+Yp0d+P1+pk2bNuSv1Ve88Z3Q3F7qJfOmsRGaA9jtdt73vvfx4osvcvbsWY4ePcr06dMHvH9tbS1vvPEGiUQCt9vNjTfeeN5ivpdi2Cy4pmYTOdBKT3V7b3Ces5Lmpufp7j5Fd/cpPJ7K927oXZLJKKGuAwQDewgE95JKRtLPWSxOMjNmcuZMD1XT/xKXS4tXiYhcjMfjwev10tXVRWtr66DKnrmrckh29hBv6saaPTQzwW02LznZy8jJXvb2B6TH3inpEmslFDpAKHTg7ZIuE/D55uH3zcPtrhjUrFrTNEl1xYk3hUlFEumZ84ZhEG/uJtkR7b+DAYbDiuHofz3gnJxFNNpIo/O/iKWawLBQUvyn5OWtGlN3Vo5G7soqsu68l45n/ovAM09TtPIhrA4PGQsKiNUGiZ3pIt4Uxl44tAGsGU/Rc6yjd8HPrt7FNA2rgXNqFu6ZeZfVlj3fQ9ZdkwhtOkO8qXf2uWdePu75l54N3vn7P5Bsb8eWn4f/Ax+4ouMZLha3G9e0abj6XJenuruJnT6Trpceq6kl0dLyTpi+a1d6W1t+XnrxUc+ihdjyLu9nKyIiY4eCc5HLUFVVxbFjxwiFQpw4cYKp7yrNMZY4HXlUVHy6X/3zhvrf09a2iZLi+/D55l32m6aW1pdhQS62jSmIh4gdPY6raniDYgDjXTPOHBNKKfp//p9hf92LiZ48Sfj11wEw167lxPHexY8WL16M1Tq8i0PFm8Lp0Nx3c9mYK8+RnZ3NggUL2LlzJ7t37yY/P5+cnEvPzksmk+zZs4cjR3oXMSssLGT58uXvWZ7lQlxV2fQcbsOSYcdMmdjtPrKyFtPRsZ3W1leZOPHjA2onkegiGNxLILCbUNchzFQ8/ZzNlonPNxe/fyFebxXJpMH+/S9gtV5+f99LpLqNVDiBc7K/3+35IiJjVV5e3hUF5wAZi4shlRpU3eb3YrHYyMycQWbmDEpK7icabSDwdoje3X2SnsgZeiJnaG56Hpvdhy9zDj7ffLzeGRct6WKaJqlgjHhTN/HGMPGmblLht3+vWHtLjZ37fe+el08s1YLp7CFljZKy9JAyIiRSEXqS3QRPbyKZCJNMRUg6w0RpxkzFsdl9lE/8FF7vha/bgsEghw8fZtq0aWRlZQ35z+1aVHDPJ+l89QXM9i4aX/x3Su9+GKvfiWtaNj1HOgjvbMJ/+9DWww9tOk3sTBfw9oKfM3JwTc/B4hrc236Lx45vTQXhHY30VLfTvbeFRFsE74oJWC6w4Glk/37CW7eCYZDzkY9gcY5cmaLLZfF4cE2fhmv6u8P0072z0mtridedfrsmeyuJllYiO3cReO5ZvDeuwHfH7VpwVETkGqTgXOQyOBwOZs+ezc6dOzlw4AAVFRXY7faR7tYVOVf/vKNjGw2NTxKLtlBT8wO83umUlNyP2102oHa6u08R7jqKkeEg98576dm2i/CWzVclOB9NzGSSjsefAMCzbClvtPbWYZ00aRIFBUO/EJSZNElF4li9vR8euGfmkepOkLG4aMyF5udMmzaNxsZGzp49y+uvv85tt9120X9n3d3dbNmyhdbW3gUwZ86cydy5cwd9p4PV6yDn/ioM+zv75+XdQkfHdjo736K4+M+w2y88KzwWaycQ3E0wsJuu8DEwU+nn7I7cdL3yjIwpGMY77SeT8Qs1NySixzpJtPdgy3EqOBeRa0J+fj41NTWDqnN+jmE1oM8H2T1HOzDjSdyzhnbWaN+SLoUFt5NIhAgG3y7p0nWQRDxIe/vrtLe/3q+kS6Z3Lk5XbrqdrtfOEj0Z6N+4xcCW58JW4KY7XEt37Djh8DG6wkdJJsIQGng/MzKmUl7+Sez2rAs+39HRwauvvko0GiUQCLB69fAt/n4tsbkzyLrrXjqeeJzAc89QuOpj2NyZeOblEz3eSaI5Qux06L1LplxCojOKxW3F4ux9W++clk0yGMM9Kxfn5KwhuRY0rAbeJcXYct2Et9UTO9NF9GQAd1X/iQ3JrjAdv/oVAJm33nLe2j9jUW+YPh1XnzsgU+Fwb5heU0tPdTXRI0fo2rCB8LZt+NasxnvrrWPqAwMREbk0Becil2nKlCkcPnyYcDjMkSNHmD179kh36YoZhoWcnPfhP1f/vHU9XV1HOHr0f2F35OBxl+N2l+PxVOB2l2OznV8zuqX1ZQCys67Hf8saerbtonvPHrKCQay+8VN+omvjRuJnzmDxeGiePZvOI0dwOBzMf3sx0qFimiax2iDdO5sx7Bb8d0/CMAwMuwXvsrG9kJdhGCxdupQ//vGPhEIhduzYwbJly87brrGxkddff51oNIrdbmfZsmVMmHDlC932Dc0BPJ5KPJ5KurtP0da+iaLCu4He/wfRaAOBwG4Cwd1Eumv77edyl+L3LcTvX4DLNeGq3/aeiiRItPfWNbWXDKzOu4jIaJef31uWpLW1lWQyecV3ciXae+jaVg8mpKJJPAsKhu18bbNlkpOznJyc5ek1L3rvTtpDtKuZzs6dtJ96EzOaJLO8iqzcBfh8c7Fme8FqYM93YytwEs9uJ+qqJdhzgnD4GKmaSL/XMSx27PYsrNYMrFb329892KweLG9/Tz9m8+JylV30mFtbW9mwYQPxeO+HvC0tLTQ3Nw/LZIBrUeHaj9P50nOYbSEan/kxE/78i1g8dlwzc4nsb6V7VzOOCZkYloGPOdM0STR1EznQSuxMF54F+Xjm9f7/cJRlXnZ7A+WakoUt20nPiU5c088vhdf529+SDASxFRXiv+eeIX/90cKSkYGrqgpXVRW+29bSU11N51NPEa87TeDZ5+jatAnfXXeRsXw5xjDfaSoiIsNPwbnIZbJarcydO5dt27ZRXV3N1KlTcV4jswqsVhfFxR8gJ3cFDQ1/IBDYSTzWTiDWTiDwziI5dkdub5juqcDjLsdm89HZuQOAvLxVODxlOMoriBw6RueT68l96IMjdUhXVaKjg8CzzwHguuN2Xj9xAoD58+fjcg3dbN94czfhtxpJtPS+Uba4baRCcay+y1skbTRzOp0sW7aMV155hVOnTlFcXExFRQXQ+4bx4MGD7NvXu4hZVlYWN954I5lDfHtsor2HVCSBo9RLXt6t1NX9O21tm8j0ziAQ3EMwsIdotOmdHQyDDM9k/P4F+HwLcDrzh7Q/lyve0Hurti3HNehbtEVERhu/34/dbicej9PZ2Ulubu5773QJ1mwnnoUFdO9sJrKvFTOaJGNJ8bAEj32lgkksJ3PxNi/F0TKXOM10O47S7TxG1HaW7mAN0cRZmpr+G5vVT+aKGcSTAbrDJ0kFoxB8py2L1UWGZwoZ3ml4M6bhdpcPer2avpqamti0aROJRIK8vDwyMjKora3l0KFDCs4HyOpwk3XvB+j42X8QfGkd8dv/Ers/G/ecPOLN3bhn5cIAh5qZMonVBYkcaCPR+vaHJQakuhPpbQzDGHB76XaTSSK7dxN6dQMYkPXBP8U56cJruthy3Xhzzy8t171rN91vvdVbouWjHz2vjOK1zDVjBoVVVUR27KDzmWdItrbR8evHCb38Cv7334t7/nytGSAiMobpnbTIIFRUVFBdXU1nZycHDx5k4cKFI92lIeV05FFR/imSyQiRSB3dkRoi3bV0R2qJRZuJx9oIxNoIBHb12y/DOw2PpxwAx7SFdG3ZT/CljWT/j/djsQ3vjIumpiaam5vxer1kZmaSmZk5LB9omLEYyXCYVDBIMtRFKhQk2dVFKhgievQoZjSKo7KSaqeTRCJBbm4ukydPHpLXTgZjhHc2Eavtfbds2Azcs/Nwz8o7b5b0taCwsJDZs2dz4MAB3nzzTXJzc3E4HGzbto36+nqgtwTOddddh802tL/OonVBQq+exuK1k/2Bqfj9i7DZf0ciHuD48X9Ob2cYVrzeGW+H5fMvWsblSpkpk0RzN7G6EPHWbkiYmKYJKRNMyP7AO7dDhzadJlob6n0OsJcM7cJjIiIjyTAM8vPzqa+vp6Wl5YqDc8Mw8MzJx+Kw0rW9gZ4jHaSiSTJvnIBhNYjH4+zbtw+r1UppaSm5ubmXXQ4s1ZMg3tSNLcuJ1d97bZLs7CGyr7fMmAG4XMV4C6ZgL/Rg5CUJW48RCu0jFDpIIhmgo3N7uj2r1UNGxlS83mlkZEzD7S7DMIb2Oqu+vp7XXnuNZDJJYWEhK1asIBKJUFtbS319PR0dHZe1APd4VnjzQ3T+8WnMhgBNT/2YCQ89isVhJev2gS843nOkne79rf0X/JyShXtWLlbf4K53U9Eo4ddfJ/TKKyTb2tOPN//v/03GDe8j6/3vx5Jx8WuIVHec8I4m7BPsdDz+OAC+29birLz8hdTHOsMw8CxejHvBAro2v0bwhRdINDXR9pP/D8ekSWR94E9wTpky0t0UEZFBUHAuMgiGYTB//nw2btzI0aNHmTZtGl7vtVcKwWp14/VOx+t9p65fMtlNd6SOSKSWSHfN22F6CxgGhQV3pLfz33EjHU/8jlSgg66NO/CtWjJs/WxsbGTjxo2kUql+jzscjnSI/u4vx9szYcxUitS5ILyri1QoRDIYItUVIhkK9f49FCIVDJHsCmFGei7dGcMgvupW6t5eqHLx4sVDMssk0Rah8/lTvWGo0Xu7rGdBARbP2K6x/15mz55NU1MTLS0tvPbaa8TjccLhMBaLhcWLFw/ZhxLv5ijxYjitpLrixM701h/Nz19DQ/3vsVhd+DJn4/MvxJc5e1gW9Xy34Mu1xOvDF33eNM30ODNN0qG5YTVwVvqHvX8iIldT3+C8qqpqSNp0Tc/BcFoJbT5LrCZIMFaLc3khm17fTFtbGwCHDh3C4XBQUlJCaWkpxcXF6euJvpJdvQt5Jpq7iTd1k+yMAvQrqWEvzMA52Y+9MAN7oQeLz9HvesFFAbm57yOVihMOH6Wr6zB2ezYZGdNwuUqHdQbr6dOnef3110mlUpSUlHDDDTdgs9mw2+1MnDiRuro6qqurWb58+bD14VpitTrJ/sCf0f6Dfye0YT3xuz6OPa//jP2+v8cvJN4SIdUVx3Bacc/IwVU1+AU/k52dhDZuJLx5M6nut+9ezMzEu3IlybZWwtu2E35tC5E9e8n6wJ/gWbr0gn3r3tdCz4lOAk8+R6qnC0dpKb477xxUn64Vhs1G5i03k7FsKaH16wm9/Aqxkydp/vZ3cM2dQ9af/An24uKR7qaIiFwGBecig1RcXExhYSFNTU28+eabrFy58orrbI4FVquHTG8Vmd533qgmEmFSqR4cjndmfVncLjKWLKVr4waCf3xl2ILzjo4OXnvtNVKpFLm5udisVrra24l2dGD09BCJRolFeujs6cESjWLr6cEajeJIJHAmkzgSCaymgc1qw2qxYrNaMTDANDFNMGwWLM7e/6+maZIKxcCwYHFlYHgyer+7MrC4PNgKc3C/bxavHDsGwPTp08nJyblU9wfMmuPCluPCcFrJuK5w3Cz0aLFYWL58OX/84x/p7OwEwOv1csMNNwzZz/ZCDJsF17RsIvtb6alu7w3O89bgy5yDw5GPxTI8H1ikokliZ0LETofwLi/B4ugde/aiDBJtPb21S0u8GA4LWC0YFuBd5QS8S4sxFxdhWHpr3o/VRWJFRC4mL693Ec+Wlpb3DBwvh7PCj+GwEtpwmuDpdnY9s5OwEcXhcFBcXExDQwOxWIyamhpqamqwWCzk5+dTWlpKaWkpHsNJ4I81pMLnL/pszXJiON65TrS4bWTe+N7rclgsdjIzZ5GZOWtIjvG91NTUsG3bNkzTpKysjOXLl/e7vp0xYwZ1dXXU1tYyd+7ca3LiyHDIX/IA7S/8gdSpDhp/90PKPvsY0LvIe091Gz1HO/DfNQmLw0oiEKXnQCuuGbnYcnqv99xz8rDluXFNGfyCn/H6ekIvv0z4zTchkQTAVlBA5upVZCxZki6v4lm2jI4nniDR0Ej7f/wn4a1byf7Qh7CX9F9DxzO/gNCGrUSPHcSW6yHnYw9hDPEdgGOVxe3Gf889eFesIPD884Rf30rPvv20NjdT9JWvqHSLiMgYot9sIoNkGAYLFy5k/fr1NDY2sn37dpYvXz4uL4Rstgzg/Fs5sz6whq6NG4geqSZa24CzfGhnWITDYTZu3Eg8GqP81CnK9+3D7OrC7IkTawqTTCVJpJIkzSRJM0Xi7e+mtXcByASQME1S0SRJu4Okw0nS7gBXBjaXF7vLR0ZpPlkLJpFRUEBGTh5dLzaB3XXB/8/OSj8nEx10dXXhdruZM2fOkB2rYRj415Zj2K/9D2feLSMjg2XLlvH6669TVFTEkiVLrsq6Aq7p2UQOtBJvCJPo7MGW5cLlGvqFV1PdcXwddrpePk2ypSc9Wzw+0YdzUu9scfeMXNyz8wZUc1f1zEXkWneuXEpPTw9dXV1DusaFo8QLy3N4Y8MGYkYKj8fDzTffjN/vJ5VK0dLcwumjNZypOU2wM0B9e4SmpiZ27dqFz+cjq9VCgS2b/KICHEUZ2Ao9vTPKnaP/3Hz8+HHefPNNACorK1myZMl5ZWlyc3MpKiqisbGRw4cPc911141EV8ccm81F9gfup+M7P6Zr22ai95zGOaEMDOg51tFbju+NBsxYitjpENBbpu3chys2vxOb//KvfUzTJHr0KKH1L9Nz4ED6ccfkSfjWrME1d+5517SuadMo+vKXCb3yKsHnnyd67DiNX/s6mbfeiu/OO7C8fQ1m9nQRP7Gxt39li7HlFA7mR3NNs2ZlkfPhD5N5660EnnkWz+LrxuV7RRGRsWz0X8GJjGLZ2dnceOONbNq0idraWpxOJ4sWLdIF0duck8twTJlG7PhROp9aT+EjHxmytmOxGBs3biTSFaZ08xsUBYKkcjy96yEZYFjsODxZOJ0ejLe/LM4MDJcH25R8zKo8ug2DrniClv0thM0o4USEWCrRW2zUAAMDw2XF2tYGbW1ggsMwyLR78bozer8yvPg8XjxuD1FnkoM7DwKwYMGCC96+fTmSXTF6DrXjnpuHxWUbl6H5OaWlpfzpn/7pZdeVvRJWrwPHRB+x2iA91e14lw1taJ5ojdC1vYFYS5j8ZidxM4zFsGDNcuIo92HLfeeugmuxhr2IyGDZbDays7Npa2ujtbV1SIPztrY2Nu7aQsyVIjMzk1tuuQW31UnXGw0kA1FsLREq4l4qmEGXJ0KrNUhnQYqWlhaCwSABd5I6WweO6FlK4iWUmqUUGy5G+1KJhw8fZteu3rVrpkyZcslSczNnzqSxsZETJ04we/bsIV0A/VpWOP8+Omb+ntShVpp/8yPKvvB1DIuBZ2EhoQ2niZ4I9G5ogGNCJq7pg7+zzkwmiezaRejll4nV1r3droF7/nwyV6/COWnSJfc3bDZ8a9fguW4Rnb/7HZG9+wi99BLdb71F9v334Zo3j/Zf/gpsSeylZTgmLyG8s2lAd1GMR/aiIvI+9cmR7oaIiAyCgnORK1RcXMzSpUvZunUrR48exeVyMXv27JHu1qjhW3sLrceP0v3666Q+8yEszisvcZFIJNi8eTPBtjZKXtlCcWsXWO1k/cn9eK6bg+HxQgywGr0zdK0GhsXSW9LCwvlvBG9854/RaJRQKJT+CgaD6T8nEgliGSZthGiLhiAKdPbuZxgGNpuNVCpFQU4+xfYrW6wMoHt3C9ETnSRDMXy3Trzi9sa6qxman+OekUOsNkj0RCeehYXpsj2XyzRNEq29dUTt+R4ADLft7ccMetxJ3AsK8EzKGvQiXyIi40l+fj5tbW20tLRQOUSLETY2NrJ582YSiQQ5OTncdNNNOO0Ogq/U9VtnwnBYsBd4KCgsoLQwA3uBh1gsRn19PWfPnj2vpIthGBQUFKRLugxl0D8UDhw4wL59+wCoqqpiwYIFl5wEUlhYSE5ODu3t7Rw5coR58+Zdra6OaVari+z330fH4R/RtXc7PUeO4po+DcfETOylXuKNYVxTsnDPzE0vInu5Uj09vQt+vvpqesFPw24nY/kyvLfeir2g4D1a6M+Wm0veZz5DZN8+On77W5Jt7bT++CfYy8qInz6NxW4j59OfpHtHN9ETAVxTs7EXaVFyERG5dig4FxkCFRUVRKNRdu7cyb59+3A6nUydOnWkuzUqZN66hPb/fBwzHib85g4yb1x2Re2Zpsn27dtpOXOWovWbKO6IYLM7yf+bz5J5y/XvbDjI9RqdTidOpzNdP7Xv6/b09PQL1ft+JZNJ4vE4RgKm1GcT7jyL44NTBz1LPNHeQ/RkZ++hzMsf3MHIFbMVerBmOzF7kiQDUSwFngHvayZN4k1hYnVBYnUhUt0J7KVe/KvLAbBm2Mm8aQLkODi74QTzZuVgtV/bi72KiAyV/Px8Dh8+TEtLy5C0V1dXx9atW0mlUhQWFrJixQrsdjupaALDYcVR4cNe4MFelNFbr/xdpbMcDgcVFRVUVFT0lnRpaeHs2bOcPXuWUChEU1NTv5Iu50L0vLy8q/7B8LlrllgsxokTJ6iurgZ6F+SeM2fOe945aRgGM2fOZMuWLRw9epSZM2di1++vASmc8QHa5/2e1O5mmn/7E8r+4dsYhpGeIDGQkmwXkuzsJLRhI+HX3rXg500r8a5cifUKa9G7587FWVVF8IUXCL38MvHTpwHw3XMPnlmVpIL19BzpoHtfC34F5yIicg1RcC4yRKZPn040GuXAgQO89dZbOJ1OJk7ULGGL0072/WsJvbiOyJvbrjg437VrF6ePHqPoxVcpCsWwO70UfPFhvMuHd7aTYRi43W7cbjcF75qtY5om3d3dhEIh3E43qQ3NJIMxIgfa8Cy4vJk953TvauotDVPhw543yE8B5IoZhoHvlolYPHYM68DezEbrgsRqgsTOhDBjqXfasluwuKz9FrJzVviJx89fRE5ERC4tP7/3Q+VAIEA0Gr2itS/61vZ+94KYFqcN301ll9WexWKhsLCQwsJCFi5cSDAYTM9Gb25uJhgMEgwGqa6uxuFwUFJSQmlpKcXFxVdU5i2RSKTL1/T09BCLxdJf54LyWCxGIpE4b9/58+czc+bMAb9WWVkZmZmZhEIhjh8/zowZMwbd7/HEavWQfe99dB74IeFje4js2YtnwfxBB+bx+npC69cTfuutdxb8LCwkc9Wt/Rb8HAoWh4Os97+fjCVLCDzzLJaMDDJvvRUAz8ICDKcV9+y892hFRERkbFFwLjKE5syZQ09PD8ePH2fr1q04HA6KiopGulsjznvTCkLrXyJ67Bjx+nrsJYOrFV1dXc2xXXsoXvcy+d1JnBlZFH7582QsmD7EPb48hmGQkZFBRkbvDJvoIoPQhtNEDrbimp6NxXN5s7DijWFiZ7rAYpCxUAstjTRr5qXfdKZiSSyOd+4s6KluJ97Qe0u/xWXFUZaJo9yHvSgDw6Za5SIiQ8HlcqWD29bWVkpLSy+7DdM0OXToEHv37gVg8uTJLF68eMhngPt8Pnw+H1VVVcRiMRoaGjh79iz19fVXVNIlHo/T0tJCc3Mzzc3NtLe3k0qlLrlPX3a7HafTyaxZs5g8efJlHdO5WedvvPEGhw8fZtq0aekPG+TSCifdQ8fiP5Da2kDL73/GxLn/B+MyfnbpBT9fWk/PwYPpx51TJpO5evUFF/wcSvbiYvI+/al+j1mcNl2ziojINUnBucgQMgyD6667jmg0yunTp9m8eTO33norublXXu96LLNlZ+OeO4funbvpfHY9+Z/+6GW3UVtby/4tWyh5eQNZ4Tgefz5FX/ki7pmXXtxoJDgmZmIr8JBo7ia8u5nM9w38zbxpmoR3NAHgmpaN1TfalxMbP8yUSaK5G3tRBsmuGLHaELHTQeLNEXL+dGr6AxLX1CxsOa7ecZDvGfQsMhERubT8/HxCoRAtLS2XHZybpsnu3bs5fPgw0Lvg5bx584Z9gXeHw0F5eTnl5eXpki7nZqMHg8FLlnS5UFD+bufujMvIyMBut+NwOC74Zbfbr/gDgoqKCvbt20ckEqGmpuayw/fxymbzknPHn9K++0d01x0ivG073hve9577mYkEkd27Ca5fT7yut1QKhoF7wQIyV63COWloav1fKdM0STR1q9a5iIhcExSciwwxi8XC8uXL2bhxI01NTWzcuJHVq1fj8/lGumsjyjVvKYH/3kLipc3k/I/7sHoHXn6kubmZt158iZJXX8FvMciaVknBI3+Dc9KEYezx4BmGQcZ1hQReOEX0eCfumbnYsl0D2jdWGyTRGsGwW/CotvmoYSZSdD57gmQwhjXbSbIj2u/5eHM3zgo/AM5JWThH3+c5IiLXnPz8fE6ePHnZdc7PrZdy6tQpABYsWDAipUb6lnRZsGABoVAoXRf93SVdbDbbBUuseL1e8vPzKSgooKCgAK/XO+zh/zlWq5Wqqip2797NoUOHqKysHJGFvMeigol30bbsSVKvnqXtqV+Scf3ii5ZVSS/4+cqrJNv7LPj5vuV4b7nlshf8HE5myiT4Ug3xxm58aytwFCs8FxGRsU3BucgwsFqt3Hjjjbzyyit0dHSwYcMGVq9ejccz8IUFrzWeJXOxZueSbG8j8N8byfnz2we0XzQa5Y2nnqVo46tkWizkVk2n4HOPYMvOHuYeXxl7gQdHuY9YbZDwjqb0gpDvuV9hBq6qHCweGxa3TtGjhWGzYM12kQzGekNzA+yFHhwTfTgmZmL16s4AEZGr7Vyd8/b2dpLJ5IBLhezZs4dTp05hGAZLlixh0qTR8WlnZmYmVVVVFy3pAr1lX86F5Pn5+ekycSNlypQpHDx4kFAoxJkzZ7S+zwDZbJlkr34/nW/9f0SaTxDasBHf2jX9tkl0dNC1cdN5C35m3nwTGStWYvWOvlDasBhYs1zEG7sJb6/Hfs+UAa8RIyIiMhoplREZJg6Hg5tvvpmXXnqJrq4uNmzYwKpVq65o8aqxzGKx4L1pJY2/f4Kmp35L2Ad2twv727cLX+grlUoROnCEaTt24EpB/pKFFH7+c1jHyOz9jEUFxBu6sOe5MVPmgEp2WNw2vEuLr0Lv5HJlXF+ExWPrLcNSlonFpV+hIiIjKTMzE6fTSTQapb29PR2kX8rJkyeprq4GYOnSpVRWjo7yFu/27pIunZ2deDweXK6B3cF2tdjtdqZNm8aBAwc4dOgQZWVlV23G+1hXWHInHSufI/nfdbQ//194b3gflowM4mfPEnr55Qss+LmKjCXXD+mCn8PBs6CAWE2AZCBG5FAbnjlaMFRERMYuvesXGUYul4tbbrmF9evXEwgE2LRpEzfccANut3tcvqnw3Hkjwd/9HNrDZPyf72EaFkyXg2iGk4jdjmmzk4hDymYj5bCTstqYeuwYDizkVs6i4JG/xeq79EJZo4nV5yTnz6Zj2N/7tmXTNMflmBhLrBl2vEv0oYaIyGhhGAZ5eXmcPXuW1tbW9wzOm5ubefPNNwGYNWvWqA3N381isZCTkzPS3bioadOmUV1dTXt7O01NTRQVFY10l8YEuz2LrBvvJrD9p/R01tLxxBOkIj39F/ycOqV3wc85c8bMdaLFacVzXSFdW+qJ7GvBOcmPNcM+0t0SEREZFAXnIsPM6/Vy00038fLLL9Pa2srTTz+N0+kkJyeH7Ozs9PerWZNypNR2tNIxfTp5h4/gtboxMcFqxbBYMOMJUj0xEoEeUqZJ+r+UQd60OUz83/9wWXXRR4uBhOYAkX2txJu7yVhUiC1ndM0mExERGa3y8/M5e/YsLS0tl6xTHgqF2Lx5M6lUirKyMubOnXsVe3ltc7lcTJ48maNHj3Lo0CEF55ehsPB2Olc+T/IPNQTf2IzNmtG74OfCBWTeOnoW/LxczslZ9BztJNHcTfitRnw3lY10l0RERAZFwbnIVZCdnc1NN93Ejh076OjoIBqN0tDQQENDQ3obu91OdnZ2OkzPycnB5/NdM2F6KpXiyJEjdC9dyNS/+iSTi0pIBrsxEzFIxTF7IiS7wsRq2kmFI6QiERJdYWrbW5nw9381JkPzvuKNYSKH2shcMQHD1j9MT/UkiBxoxYynSE7OUnAuIiIyQOdmmbe0tFz07q1YLMamTZuIxWLk5OSwbNmya+b6arSoqqri2LFjNDY20tbWRm5u7kh3aUxwOHLwX38bgb2/Jnq2Ff+td5A5yhb8HAzDMPAuLabzuRPEaoLEznbhKPVecbupaIJEWw+JtgjOcj9WX2/ZGjNp9r6u6qmLiMgQU3AucpXk5+dz++23k0gk6OzspKOjg/b2djo6Oujs7CQej9Pc3Exzc3N6n8LCQhYsWDCqb88dqLq6Orq7u3E6nUyaNAmrzYbVd/4FdMb17/w5Ho/z5gsvYHGP7brwZtIk9NpZUuE4ker282o9du9rwYynsOW6cFSOjfrtIiIio0FOTg4Wi4VoNEooFML3rnVQUqkUr7/+OsFgELfbzYoVK7DZ9BZoqHm9XsrLy6mpqaG6upobbrhhpLs0ZhQW3kHHB7cQNVO0Z57G4enAztgOzgFsOS5cM3KIN4QxHL2TRkzTJLy9AYvXgdXnwJrZ+3WhOzRTsSSJ5u7eoLw9QqKth1RXPP28xWVLB+fxhi6Cr57Glu3ElufGluPCluvGmu1SmC4iIldEV40iV5nNZiMvL4+8vHfC02QySTAY7Bemt7W10dTUxLp165g0aRJz587F4/GMYM8HzzRNDh8+DPTWwRxvb1gNq4FnQX5vrcf9LbimZWFx9v4MkqEYPYc7APAsKtQMOBERkctgtVrJzc2lpaWFlpaW84LzXbt20dDQgNVqZcWKFWP2WmosmDlzJjU1NdTV1REMBs/7fyEX5nTkUVLyZzQ0/IFw11FOdH0bb+YMigrfT0bGpJHu3hXJWFgIFgPD0nt9mwrH6TnScd52FrcNi8+Bs9KPu6p3wlCiJULw5brztrX6HFhzXFj61E1PdEQhZb49G72nT8MGtmwnGUuKsRfo376IiFy+8ZVeiYxSVqs1XaZl0qTeC+Suri727NlDXV0dJ0+epK6ujpkzZ1JVVTXmgueWlhba29uxWCxMnTp1pLszIpyTsug51E6ivYfI3lYyru+t/9m9uxlSJvaSDBwlV34Lq4iIyHiTn5+fDs4nT56cfvzYsWMcPXoUgGXLlql8yDDLysqitLSUs2fPUl1dzZIlS0a6S2NGfv5q/P5FNDe/QFv7a3SFqjkeqibTN5fiontxuyeOdBcH5d3lCQ2LgWdePslQjGQwRjIUw4wmSUUSpCKJfuG2LdeF1e/AluvGlvv2DPIcFxaH9bzXcc/OxVnhI9EWSZdySbT1YEaTJNp6+vWj50g7PUc73jUz3YlhHdi6RCIiMr6MrfRNZBzxer3ccMMNtLS0sGvXLtra2ti3bx/Hjx9n/vz5lJeXj5nZydXV1QBUVlbico3P+t2GxcBzXSHBl2qJHG7HVZWDGU8SPRkAIGNR4Qj3UEREZGzqW+f8nIaGBnbs2AHA3LlzmThxbAaPY83MmTM5e/Ysp06dYsqUKWRnZ2OxKJAcCIcjhwkT/gf5BbfR1PQcHR3bCAX3EQruw+9fSFHRvbhcJSPdzSti8djxLOhfhiYVTZAMxUkFY1j9jne2ddnI/pOBTbgxDCNd9sVZ4Qd673hNdcVJtEWw+t8p+xhviVx0Zrotz41nXj4Wj/3dLyEiIuOUgnORUS4/P581a9ZQW1vLnj176O7uZuvWrRw5coSFCxem3yyOVsFgkLNnzwK9C0eNZ44SL/ZSL/GzXYR3NaVnvzgn+bHlju3FT0VEREbKufJ3oVCInp4eYrEYW7ZswTRNKioqmDVr1gj3cPzIz89P3wHw4osvYrFY8Pl8+P3+9PesrCy8Xq8C9YtwOvKYWPYxCgpup6npOTo73yIQ2EUguJusrMUUFt6Ny1k00t0cMhanrbeEYd7QXgv3DdP78iwowDHBe8GZ6Ym2Hjx9JrNEDraRDETfmfGumekiIuOOgnORMcAwDCoqKpgwYQKHDx/m0KFDtLW1sX79esrLy5k3bx5e7+gs83HkyBEASkpK8Pv9I9ybkZexqJDO+i5iNUH8d1Ziy3HhKMsc6W6JiIiMWU6nE5/Pl/6w/uDBg8TjcfLy8liyZMmYuUPvWrFw4UJ27NhBZ2cnyWSSzs5OOjs7+22jQP29uZxFlE/8BIUFd9LY+AyBwC46O96ks3MH2dlLKSy8G6cj770bkn6sGXasGf4LzkxPdcX7lYKJ1QWJN3W/s/O5melvl49xTs1O128XEZFrk4JzkTHEZrMxe/ZsJk2axL59+zh58iS1tbWcPn2aqqoqZs2ahd0+em4tjEajnDx5EtBs83NsOS7cc/KwZfXeDmrP10JFIiIiVyo/P59gMMhbb71FKpUiIyODFStWYLWeXw9Zhldubi5r167FNE3C4TCBQOC8LwXqA+dylVBR8RkikToaGp8hFNxHR/tWOjq2k5NzA4UFd+Jw5Ix0N8esi81MB3DPzsNW0H3BmekWtw3ntOwR6LGIiFxNCs5FxiCPx8PSpUuZNm0au3btorm5mUOHDnHy5Enmzp3LpEmTRsWbi2PHjpFMJsnOzqawUDW8z8lYqJ+FiIjIUMrPz+fEiROkUilsNhsrVqwYt+uqjBaGYeD1evF6vZSWlqYfV6A+OG73RCZV/jXh8Ekam56mK1RNe9tmOjq2kpuzgoKCO7Dbr+27O03TJBZrJRKpJRI5jdXqwe0px+OeiNU69JNRHGWZ6TtDTdMkFY73BuetEQyLkb6bxTRNurbW45qShb0wY8j7ISIiI2fUB+ehUIh/+Id/4KmnnqK5uZkFCxbwL//yLyxevBiArq4u/u7v/o6nn36atrY2KioqePjhh/nMZz6TbiMajfKFL3yBJ554gkgkwq233soPf/hDJkyYkN6mo6ODhx9+mGeffRaAe+65h+9973tkZWVd1eMVuRw5OTnceuutnD17ll27dtHV1cWbb77J0aNHWbhwIUVFI1f/MJlMcvToUaB3trlukxYREZHhUlDwzoKD73vf+8jO1kzQ0UqB+pXJyJjE5El/S1fXURqbniHcdZTW1ldpa3+NvNxbKChYi8029ssAmqZJNNZEpLvu7aC8lu5IHalk5ILbO5z5uN3leNzluN3luN0TsdmGLsQ2DAOr14HV68BZ7uv3XOxUgOixTqLHOnGUZeJZWIAtWx/ciYhcC0Z9cP6Xf/mXHDhwgF/+8peUlJTwq1/9ilWrVnHo0CFKS0v53Oc+x4YNG/jVr35FRUUFL730Ep/97GcpKSnh3nvvBeCRRx7hueee4ze/+Q25ubl8/vOf56677mLnzp3p2zcfeOABzpw5w7p16wD45Cc/yYMPPshzzz03YscuMhCGYTBhwgSKi4s5duwY+/fvp7Ozk1dffZXS0lIWLFiAz+d774aGWG1tLT09PbjdbiZOnHjVX19ERETGD6/Xyw033IDdbqe4uHikuyODMNSBut/vZ/LkyUyaNAmbbdS/7b1sXu80Jmd8ga6uahobn6a7+xQtLS/S1raRvPxVFOSvGZZZ2JcjlYpSW/fvRKNNWCxOLBYHFsPR+93iwHj7+zuP2YnF2ohE6ohETpNKRc9r0zCsuNxluF0TSCYjRCK1xGKtxKItxKItBDp3pLd1OPJweypwuye+HahPxGYb+nWh7EUZuKZn03O0g9jpELEzIZyTs/DMz8fqPb8EjIiIjB2j+goiEonwhz/8gWeeeYYVK1YA8Nhjj/H000/zox/9iH/6p39i27ZtfPSjH+Wmm24CegPvn/zkJ+zYsYN7772XQCDAT3/6U375y1+yatUqAH71q19RVlbGyy+/zNq1a6murmbdunVs376dJUuWAPBv//ZvLFu2jCNHjjB9+vQROX6Ry2G1WqmqqqKyspL9+/dz7Ngxzp49S319PdOmTWP27Nk4nc6r0hfTNDl8+DAA06dPV31RERERGXb6oP7aNNhAvaOjgx07drB//36mTp3KtGnTrrnyPYZhkJk5E693BqHQfhoan6YncprmpudpbX2Vgvw15OWtwmq9+sdtmiZnzvyKYGDPoNswLHbc7rL0DHKPeyJOZwkWS/8YI5HoIhKpoztS2xu6d9f0hulvf50XprvL3y7xMjRhusVjx7usBPfMXMK7monVBoke7yR6KoC7KgfPggIM27V5B4SIyLVuVAfniUSCZDJ53gWO2+1my5YtANxwww08++yz/MVf/AUlJSVs3LiRo0eP8i//8i8A7Ny5k3g8zpo1a9L7l5SUMHv2bLZu3cratWvZtm0bfr8/HZoDLF26FL/fz9atWy8anEejUaLRdz4FDwaDAMTjceLx+ND8EEaxc8c4Ho51LLFYLMybN4/Kykr27t1LfX091dXVnDhxglmzZjFlypRhv3W1sbGR9vZ2bDYb5eXlgx4jGmMyHmicy9WgcSZjlcauXIrT6aSgoKBfqR7TNOnu7qa+vp4jR44QDofZt28fBw8epKKigunTp5OZ+U4pk2tljLndM6isqCIU2kNT83NEo/XUNzxFU/NL5OetJSfnJiyWqzf7ub19E23tWzGwUFLyIDabl1QqTioVxTRjpFJvf5kxzD5/tlkzcbsn4nKX43QUYBj9J+AkkybJ5Lv/Xzlxuabick2F7HPbhYlEThPpqaUnUkekp45YrIWeaDM90WY6Ot9K72235+J2T8Tt6g3SXa5BhukeC+4birBP9xPZ3UKiuZtoQxf2eTkY8ZEvW3mlY32s/xsRERkMwzRNc6Q7cSnLly/H4XDw+OOPU1hYyBNPPMFHPvIRpk6dypEjR4jFYnziE5/gP//zP7HZbFgsFv793/+dBx98EIDHH3+cj33sY/0CboA1a9ZQWVnJT37yE77+9a/zi1/8Il2P+Zxp06bxsY99jEcfffSCfXvsscf46le/et7jjz/+OB7PyN4WJ3JOd3c3bW1txGIxAOx2O7m5uXg8nmGrO97Q0EB3dzd+v5+8vLxheQ0RERERkUs5Nyu9s7Oz3/vBjIwMsrKyRs0M9FAoRE9PDz6fb4juEE1hsZ7CbtuFYQkAYJpuEvF5JJNVDPf8OcNoxun8bzBSxOPXk0zMHdbXG7goFksrhqUVi6UVi9GGYQlecEsz5SVl5pFK5WGmer/DZYwXEzxhK0mrSdSdGpruj7Du7m4eeOABAoHAiJQCFREZCaN6xjnAL3/5S/7iL/6C0tJSrFYrCxcu5IEHHmDXrl0A/Ou//ivbt2/n2Wefpby8nM2bN/PZz36W4uLidGmWCzFNs19oeKEA8d3bvNujjz7K3/7t36b/HgwGKSsrY82aNePiF0k8Hmf9+vWsXr0au90+0t2RS0ilUpw6dYoDBw7Q09NDKpUiIyOD+fPnD/kCuIFAgHXr1pGZmckdd9yB1zv4Wx81xmQ80DiXq0HjTMYqjV0ZCqZp0tLSwuHDh2loaAB6x1ZWVhaTJ0/mwIEDrFmzZkTGWCgUYt26dbhcLmKxGIWFhcyePXtI3k+aZpLOzjdpaXmeWLwVOInd1k5+/h1kZy/HMIY+DkgkQpw4+XXi8Tx8vgWUTfjksE3WGQrJZDeRntP0nCvz0lNLLNby9rPdQN3bX31npk/E9fYM9eGomT5crvR8eu4OexGR8WTUB+eTJ09m06ZNhMNhgsEgxcXF3H///VRWVhKJRPj7v/97nnrqKe68804A5s6dy549e/j2t7/NqlWrKCoqIhaL0dHRQXZ2drrd5uZmli9fDkBRURFNTU3nvXZLSwuFhYUX7ZvT6bzgjAC73T6uLuzH2/GOVVVVVUyaNIlDhw5x+PBhWlpaWL9+PZMnT2bu3Lm43e4heZ3jx49jsVgoKyvr92/uSmiMyXigcS5Xg8aZjFUau3KlSktLKS0tpbOzk+rqampra2lra6OlpSVdDz07Oxufz4fP5yMzM/OqLCp66NAhADweDz09PZw9e5azZ89SUVHBnDlz+pWVuXx2CgpWkJe3nI6O12lqep54vIPGxt/Q1r6eosK7yc5eel45lMEyzSR1dT8jmQjgdhVTUf6XWK2je3FMu92Py+WHrNnpx5LJbrojdUQitUS6a+mO1BKLNpNMdNAV6qArtDe9rTdzBsVFH8DjqRiB3g/OYM+nOgeLyHg06oPzczIyMsjIyKCjo4MXX3yRb33rW+la4u+u12y1Wkmlem+HWrRoEXa7nfXr13PfffcBvWUkDhw4wLe+9S0Ali1bRiAQ4M033+T6668H4I033iAQCKTDdZFrgcPhYP78+UyZMoU9e/ZQV1fHiRMnqK2tZdasWUyfPv2K3iBEIhFqamqA3qBeRERERGQ0ycrKYtmyZcybN48jR45w9OhR4vE4dXV1nDlzpt+2Xq83HaT7/f70d4djaMLgtrY26up6ZzPffPPNAOzbt4+zZ89SU1NDbW0tlZWVzJ49+4ru4rRYbOTmriQ7exlt7a/R3PwC8Vgbp0//gqbmFygqvIesrMUYxpWtg9TY+DRdXUewWJxUVHx2RBYlHQpWq4dMbxWZ3nfezyST3UQip+mO1BDp7p2dHo020RWq5ljoa2RlLaao6P04nQWXaFlERMaaUR+cv/jii5imyfTp0zl+/Dhf/OIXmT59Oh/72Mew2+2sXLmSL37xi7jdbsrLy9m0aRP/+Z//yXe/+10A/H4/H//4x/n85z9Pbm4uOTk5fOELX2DOnDnpUi4zZszgtttu4xOf+AQ/+clPAPjkJz/JXXfdddGFQUXGMq/Xyw033EBLSws7d+6kvb2dvXv3cvz4cebNm0d5efmgbqk8evQoqVSKvLw88vPzh6HnIiIiIiJXzuPxsGDBAqZPn87TTz/NnDlzCIfDBAIBgsEg8Xicrq4uurq6qK+vT+9nt9u5+eabr3gdH9M02b17NwCVlZXpOzVXrlxJW1sb+/bto6GhgZMnT1JTU8PkyZOZNWvWFa2lZbE4yM+7ldycG2ht20hz8zpi0Wbq6v6dpubnKS56Pz7fgkG9DwgEdtPcvA6ACWUfxeUqGXQ/RyOr1YPXOx2v9518IBptobHpGTo736Sz8y06AzvJzV1JYcFd2O3XfulWEZHxYNQH54FAgEcffZQzZ86Qk5PDBz/4Qb72ta+lbxP6zW9+w6OPPsqHP/xh2tvbKS8v52tf+xqf/vSn0238n//zf7DZbNx3331EIhFuvfVWfvGLX2C1vnNL2q9//Wsefvhh1qxZA8A999zD97///at7sCJXWX5+PmvXrqWmpoa9e/cSDofZunUrR48eZeHChZf1hiCRSHDs2DFAs81FREREZGyw2+1kZGRQVVWVfo9pmiY9PT3pEP3cV0dHB9FolG3btnH77bdf0Z2a9fX1NDc3Y7FYmDu3/+KZubm53HzzzbS0tLBv3z6ampo4duwYJ06cYOrUqcycOfOKyixaLE4K8teSm7OS1tZXaG55kWhPAzU1P8KbOZMJpQ/gdF68ZOm7RaNN1J3+GQB5+avIzlo86L6NJU5nPuUT/5KC/DU0NDxFKHSAttYNdLRvJT9/Nfn5a7Bah6YcpoiIjIxRH5zfd9996RIrF1JUVMTPf/7zS7bhcrn43ve+x/e+972LbpOTk8OvfvWrQfdTZKwyDIPKykrKysqorq7m0KFDtLa28tJLLzFx4kRKS0vJyckhMzPzvLJIfZ08eZJYLEZGRgYTJky4ikcgIiIiIjJ0DMPA7XbjdrspKipKPx6NRnnhhRcIhULs37+fBQsWDKr9VCrFnj17AJg2bRoZGRkX3C4/P59bb72VpqYm9u3bR0tLC0eOHOH48eNMmzaNGTNm4HINvhyK1eqisPBO8vJuprn5RVpaXqQrdIgjR75CQcHtFBTcjsVy6bI0yWSUmpofkUr2kJExheKiDw66P2OV2z2RSZP+hlDXYRoa/kCku4ampv+mtW0jhYV3kZuzAotF9cFFRMaiUR+ci8jVYbPZmDNnDpMnT2bv3r2cOnWKurq6dN1Fm81GVlYWOTk56S+fz4fFYsE0TY4cOQL0zja/VMAuIiIiIjIWOZ1OFi9ezObNm6murqasrGxQJVtOnTpFIBDAbrcza9as99y+sLCQVatW0djYyL59+2hra6O6uppjx44xffp0qqqqcDqdgzkkoLcMSXHxn5CTs5yzZ58gFDpIU9N/09GxndLSD+Hzzb3gfqZpcubsf9LTcxab3Ud5+aewWMZvxJDprcI75e8JBHbS2Pg00WgT9Wd/Q0vLeoqK7iU7a+mgyuCIiMjIGb+/1UTkgjweD8uWLWP69OmcPHmS9vZ2Ojo6SCQStLa20tramt7WarWSnZ2Nx+MhFApht9uZNGnSCPZeRERERGT4TJgwgYqKCmpqati+fTu33XbbZZVsSSQS7N+/H4DZs2cPOPA2DIPi4mKKioqor69n3759dHR0cPDgQY4ePUpVVRXTp0+/ooVLnc5CKiv/hkBgJ/X1vyUWa+XUqe/h882jtPTPcTj6f0jQ1raBzo43wbBQPvFT2O1Zg37ta4VhGGRlXYfPN5+OjtdpbHqudyHWup/R1raJKZP/L4XnIiJjiIJzEbmgc7PKofd20lAoRHt7O21tbekwPZlM9gvSp06dmq4NKSIiIiJyLVq0aBGNjY0Eg0EOHDjA/PnzB7zvkSNH6O7uxuPxMHXq1Mt+bcMwKC0tpaSkhDNnzrBv3z4CgQD79+/nyJEjzJgxg2nTpg36mvxc8JuZOYempudoaV1PMLiXUNchCgvvJj9vNRaLjXD4BPX1vwOgpPiDeL3TBvV61yqLxUZu7kqys5fS0voKzc3ryPTOUGguIjLGKDgXkfdksVjw+/34/X4qKyuB/mF6e3s78XicGTNmjHBPRURERESG17mSLa+99hqHDh2irKyM3Nzc99wvGo1y6NAhAObOnXtFi4sahkFZWRkTJkygrq6O/fv3EwwG2bt3L4cPH2bmzJlMnTp10K9htTopKflTcnKWc+bsrwl3HaWx4Uk6OrZSVHgv9fW/xTST+LOuIy9v9aCP41pnsTgpLLiD3JwVGIbiFxGRsUZnbhEZlAuF6SIiIiIi40FZWRnl5eXU1taybds2br/9dqxW6yX3OXDgAPF4nKysLCoqKoakH4ZhUF5eTllZGbW1tezfv5+uri52796dDtCnTJnynn27GJerhMmTvkBH53Ya6v+LaE8jtbU/AcDpKqJswkc1i3oAbDbvSHdBREQGQSv4iYiIiIiIiFymRYsW4XQ60yVbLqWrq4tjx44BMH/+fCyWoX0rbrFYqKys5M4772TJkiVkZGQQiUTYuXMnzz33HMeOHSOZTA6qbcMwyMleRlXVP5GbdzMYBhari4ryz2C1uob0OEREREYTzTgXERERERERuUwul4vFixezZcsWDh06xIQJEy5asmXfvn2kUikKCwspLi4etj5ZrVYmT55MRUUFJ06c4ODBg3R3d/PWW29x6NAh5syZQ0VFxaCCe6vVw4TSB8jPX4OBBYcjZxiOQEREZPTQjHMRERERERGRQZg4cSITJ07ENE22b99+wVnd7e3t1NTUAL2zza9GaROr1cq0adO4++67WbhwIS6Xi3A4zPbt23n++eepqakhlUoNqm2nI0+huYiIjAsKzkVEREREREQG6brrrsPpdBIIBDh48OB5z+/ZsweA8vLyAS0iOpRsNhtVVVXcc889zJ8/H4fDQSgUYuvWrbzwwgvU1dVhmuZV7ZOIiMhYoeBcREREREREZJBcLhfXXXcdAAcPHqS9vT39XENDA42NjVgsFubNmzdSXcRmszFz5kzuvfde5s6di91uJxgMsmXLFv74xz9y5swZBegiIiLvouBcRERERERE5AqUl5dTVlbWr2SLaZrp2eZTp07F6/WObCcBu93O7Nmzuffee5k9ezY2m43Ozk42b97M+vXrCYfDI91FERGRUUPBuYiIiIiIiMgVWrx4MQ6Hg87OTg4dOkRNTQ0dHR3Y7XZmzZo10t3rx+FwMHfuXO69915mzpyJ1WqltbWVdevW0djYONLdExERGRUUnIuIiIiIiIhcIZfLxeLFiwE4cOAAu3fvBmDmzJm4XK6R7NpFOZ1O5s+fz5133kl2djbRaJRXX32VgwcPqnSLiIiMewrORURERERERIbAxIkTmTBhAqZp0tPTg9vtZvr06SPdrffk9XpZvXo1kyZNAmDv3r289tprxGKxEe6ZiIjIyFFwLiIiIiIiIjIEDMNIl2wBmDt3LjabbYR7NTA2m40lS5Zw/fXXY7FYOHPmDC+++CKdnZ0j3TUREZERoeBcREREREREZIi43W5uueUWli5dmp7BPVYYhsGUKVNYtWoVHo+HUCjESy+9RG1t7Uh3TURE5KpTcC4iIiIiIiIyhHJycpg0aRKGYYx0VwYlLy+P2267jcLCQhKJBK+//jo7d+4kmUyOdNdERESuGgXnIiIiIiIiItKPy+Xi5ptvZubMmQAcOXKEDRs2EIlERrhnIiIiV4eCcxERERERERE5j8ViYf78+dx4443YbDaam5tZt24dLS0tI901ERGRYafgXEREREREREQuqqysjNtuuw2fz0ckEmHDhg2Ew+GR7paIiMiwUnAuIiIiIiIiIpfk8/lYu3YteXl5JBIJdu7cOdJdEhERGVYKzkVERERERETkPdntdq6//noMw+DMmTOcPXt2pLskIiIybBSci4iIiIiIiMiAZGVlUVVVBcCOHTtIJBIj3CMREZHhoeBcRERERERERAZs9uzZeDwewuEwBw8eHOnuiIiIDAsF5yIiIiIiIiIyYHa7nUWLFgFQXV1NIBAY4R6JiIgMPQXnIiIiIiIiInJZJkyYQElJCalUih07dmCa5kh3SUREZEgpOBcRERERERGRy2IYBosWLcJqtdLU1ERtbe1Id0lERGRIKTgXERERERERkcuWmZnJrFmzANi1axexWGyEeyQiIjJ0FJyLiIiIiIiIyKDMmDGDzMxMenp62Lt370h3R0REZMgoOBcRERERERGRQbFarSxevBiAY8eO0dbWNsI9EhERGRoKzkVERERERERk0IqKiigvLwfgrbfeIpVKjXCPRERErpyCcxERERERERG5IgsXLsRut9Pe3s7x48dHujsiIiJXTMG5iIiIiIiIiFwRt9vN3LlzAdi7dy+RSGSEeyQiInJlFJyLiIiIiIiIyBWbOnUq2dnZxONxdu/ePdLdERERuSIKzkVERERERETkilksFq6//noAampqaGpqGuEeiYiIDJ6CcxEREREREREZErm5uUydOhXoXSg0mUwO+WuYpolpmkPeroiISF+2ke6AiIiIiIiIiFw75s6dS11dHcFgkF27dlFRUUFmZiZOpxPDMC6rrXg8TmdnJx0dHbS3t9PR0UEgEMDhcFBYWJj+8nq9l922iIjIpSg4FxEREREREZEh43Q6WbhwIdu2bePYsWMcO3YMAJvNhtfrJTMzE6/X2+/PHo+HeDxOR0dHv5A8GAxe8DV6enqora2ltrYWAI/H0y9Iz8jIuGrHKyIi1yYF5yIiIiIiIiIypCoqKujp6aG+vp5QKER3dzeJRILOzk46OzvP294wjIuWX3G73WRnZ5OdnU1OTg5ZWVl0d3fT1NREc3Mzra2tdHd3c+rUKU6dOgWA1+vtF6S73e7hPNyLisViHD58mKKiIgoKCkakDyIiMjgKzkVERERERERkSBmGwYwZM5gxYwYAyWSSrq6u9FcoFEp/D4fDpFIpADIyMsjJyUmH5NnZ2RcMvTMzMyksLAQgkUjQ0tJCU1MTTU1NtLe3p1/nxIkTAPh8vnSIXlBQgMvlGtbjTyQSHDt2jIMHDxKLxWhsbGT16tUqJyMiMoYoOBcRERERERGRYWW1WvH7/fj9/vOeS6VSRCIRbDYbTqfzstu22WwUFxdTXFwM9M7y7huknyv5EgwG02VjsrKy+gXpDofjyg6wz7GcOnWK/fv3093dDfSG/FVVVUPSvoiIXD0KzkVERERERERkxFgsliGtSe5wOCgtLaW0tBSAaDRKc3NzOkgPBALpkjFHjhwBICcnJx2k5+fnY7fbL+s1TdPk9OnT7N27l1AoBPTWXZ8zZw6VlZVYLJYhOz4REbk6FJyLiIiIiIiIyDXL6XRSVlZGWVkZ0Luw6LkQvampiVAoRHt7O+3t7VRXV2MYBrm5uekgPS8vD5vt4vFJY2Mje/bsob29HegN7mfPns2UKVMuuZ+IiIxuOoOLiIiIiIiIyLjhcrkoLy+nvLwcIL3Q6LmvcDhMa2srra2tHDx4EIvFQl5eXjpIz83NxWq10tbWxp49e2hqagJ6S8ZUVVVRVVU1ZKVfRERk5Cg4FxEREREREZFxy+PxUFlZSWVlJQBdXV39gvRIJEJzczPNzc3s378/Xa/93Axzi8XC1KlTmTVr1rAvOioiIlePgnMRERERERERkbd5vV68Xi+TJ0/GNE1CoVC/ID0ajaZD88rKSubMmYPX6x3hXouIyFBTcC4iIiIiIiIicgGGYeDz+fD5fEydOhXTNOns7KSjo4Pc3Fz8fv9Id1FERIaJgnMRERERERERkQEwDIPs7Gyys7NHuisiIjLMLCPdARERERERERERERGR0UTBuYiIiIiIiIiIiIhIHwrORURERERERERERET6UHAuIiIiIiIiIiIiItKHgnMRERERERERERERkT4UnIuIiIiIiIiIiIiI9KHgXERERERERERERESkDwXnIiIiIiIiIiIiIiJ9KDgXEREREREREREREelDwbmIiIiIiIiIiIiISB8KzkVERERERERERERE+lBwLiIiIiIiIiIiIiLSh4JzEREREREREREREZE+FJyLiIiIiIiIiIiIiPSh4FxEREREREREREREpA8F5yIiIiIiIiIiIiIifSg4FxERERERERERERHpQ8G5iIiIiIiIiIiIiEgfCs5FRERERERERERERPpQcC4iIiIiIiIiIiIi0oeCcxERERERERERERGRPkZ9cB4KhXjkkUcoLy/H7XazfPly3nrrrX7bVFdXc8899+D3+8nMzGTp0qXU1dWln49Go/z1X/81eXl5ZGRkcM8993DmzJl+bXR0dPDggw/i9/vx+/08+OCDdHZ2Xo1DFBEREREREREREZFRZNQH53/5l3/J+vXr+eUvf8n+/ftZs2YNq1at4uzZswCcOHGCG264gaqqKjZu3MjevXv5h3/4B1wuV7qNRx55hKeeeorf/OY3bNmyha6uLu666y6SyWR6mwceeIA9e/awbt061q1bx549e3jwwQev+vGKiIiIiIiIiIiIyMiyjXQHLiUSifCHP/yBZ555hhUrVgDw2GOP8fTTT/OjH/2If/qnf+LLX/4yd9xxB9/61rfS+02aNCn950AgwE9/+lN++ctfsmrVKgB+9atfUVZWxssvv8zatWuprq5m3bp1bN++nSVLlgDwb//2byxbtowjR44wffr0q3jUIiIiIiIiIiIiIjKSRnVwnkgkSCaT/WaPA7jdbrZs2UIqleL555/nS1/6EmvXrmX37t1UVlby6KOP8v73vx+AnTt3Eo/HWbNmTXr/kpISZs+ezdatW1m7di3btm3D7/enQ3OApUuX4vf72bp160WD82g0SjQaTf89GAwCEI/HicfjQ/VjGLXOHeN4OFYZGRpjMh5onMvVoHEmY5XGrgw3jTEZL650rOvfiIiMR6M6OM/MzGTZsmX84z/+IzNmzKCwsJAnnniCN954g6lTp9Lc3ExXVxff/OY3+ad/+if++Z//mXXr1vGBD3yADRs2sHLlShobG3E4HGRnZ/dru7CwkMbGRgAaGxspKCg47/ULCgrS21zIN77xDb761a+e9/hLL72Ex+O5wqMfO9avXz/SXZBrnMaYjAca53I1aJzJWKWxK8NNY0zGi8GO9e7u7iHuiYjI6Deqg3OAX/7yl/zFX/wFpaWlWK1WFi5cyAMPPMCuXbtIpVIA3HvvvXzuc58DYP78+WzdupUf//jHrFy58qLtmqaJYRjpv/f988W2ebdHH32Uv/3bv03/PRgMUlZWxpo1a/D5fJd9rGNNPB5n/fr1rF69GrvdPtLdkWuQxpiMBxrncjVonMlYpbErw01jTMaLKx3r5+6wFxEZT0Z9cD558mQ2bdpEOBwmGAxSXFzM/fffT2VlJXl5edhsNmbOnNlvnxkzZrBlyxYAioqKiMVidHR09Jt13tzczPLly9PbNDU1nffaLS0tFBYWXrRvTqcTp9N53uN2u31cXXSNt+OVq09jTMYDjXO5GjTOZKzS2JXhpjEm48Vgx7r+fYjIeDTqg/NzMjIyyMjIoKOjgxdffJFvfetbOBwOFi9ezJEjR/pte/ToUcrLywFYtGgRdrud9evXc9999wHQ0NDAgQMH0guKLlu2jEAgwJtvvsn1118PwBtvvEEgEEiH6wNhmiYwfj6JjcfjdHd3EwwG9UtUhoXGmIwHGudyNWicyVilsSvDTWNMxosrHevnco5zuYeIyHhgmKP8rPfiiy9imibTp0/n+PHjfPGLX8TpdLJlyxbsdjtPPfUU999/Pz/4wQ+4+eabWbduHY888ggbN27khhtuAOAzn/kM//3f/80vfvELcnJy+MIXvkBbWxs7d+7EarUCcPvtt1NfX89PfvITAD75yU9SXl7Oc889N+C+njlzhrKysqH/IYiIiIiIiIiIjLDTp08zYcKEke6GiMhVMeqD89/97nc8+uijnDlzhpycHD74wQ/yta99Db/fn97mZz/7Gd/4xjc4c+YM06dP56tf/Sr33ntv+vmenh6++MUv8vjjjxOJRLj11lv54Q9/2C/kbm9v5+GHH+bZZ58F4J577uH73/8+WVlZA+5rKpWivr6ezMzMS9ZGv1acq+l++vTpcVHTXa4+jTEZDzTO5WrQOJOxSmNXhpvGmIwXVzrWTdMkFApRUlKCxWIZhh6KiIw+oz44l9ErGAzi9/sJBAK6yJRhoTEm44HGuVwNGmcyVmnsynDTGJPxQmNdROTy6WNCEREREREREREREZE+FJyLiIiIiIiIiIiIiPSh4FwGzel08pWvfAWn0znSXZFrlMaYjAca53I1aJzJWKWxK8NNY0zGC411EZHLpxrnIiIiIiIiIiIiIiJ9aMa5iIiIiIiIiIiIiEgfCs5FRERERERERERERPpQcC4iIiIiIiIiIiIi0oeC8zHgG9/4BosXLyYzM5OCggLe//73c+TIkX7bmKbJY489RklJCW63m5tuuomDBw+mn29vb+ev//qvmT59Oh6Ph4kTJ/Lwww8TCAT6tdPR0cGDDz6I3+/H7/fz4IMP0tnZecn+9fT08NBDDzFnzhxsNhvvf//7L7jdpk2bWLRoES6Xi0mTJvHjH/94QMf/wx/+kMrKSlwuF4sWLeK1117r9/yTTz7J2rVrycvLwzAM9uzZM6B25R3jeYxt3ryZu+++m5KSEgzD4Omnnz5vm4ceegjDMPp9LV269D3bltHlWhjnDQ0NPPDAA0yfPh2LxcIjjzwy4OPXuXT4jecxpnPp2HctjN8nn3yS1atXk5+fj8/nY9myZbz44osDOn6dI4ffeB5jOkeOH1dznH/ta19j+fLleDwesrKyBtzH/fv3s3LlStxuN6Wlpfyv//W/6Lv0na43RUTeoeB8DNi0aRP/83/+T7Zv38769etJJBKsWbOGcDic3uZb3/oW3/3ud/n+97/PW2+9RVFREatXryYUCgFQX19PfX093/72t9m/fz+/+MUvWLduHR//+Mf7vdYDDzzAnj17WLduHevWrWPPnj08+OCDl+xfMpnE7Xbz8MMPs2rVqgtuc+rUKe644w5uvPFGdu/ezd///d/z8MMP84c//OGSbf/2t7/lkUce4ctf/jK7d+/mxhtv5Pbbb6euri69TTgc5n3vex/f/OY3L9mWXNx4HmPhcJh58+bx/e9//5Lb3XbbbTQ0NKS/XnjhhUtuL6PPtTDOo9Eo+fn5fPnLX2bevHkDPnadS6+O8TzGdC4d+66F8bt582ZWr17NCy+8wM6dO7n55pu5++672b179yXb1jny6hjPY0znyPHjao7zWCzGn/3Zn/GZz3xmwP0LBoOsXr2akpIS3nrrLb73ve/x7W9/m+9+97vpbXS9KSLShyljTnNzswmYmzZtMk3TNFOplFlUVGR+85vfTG/T09Nj+v1+88c//vFF2/nd735nOhwOMx6Pm6ZpmocOHTIBc/v27elttm3bZgLm4cOHB9S3j370o+a999573uNf+tKXzKqqqn6PfepTnzKXLl16yfauv/5689Of/nS/x6qqqsy/+7u/O2/bU6dOmYC5e/fuAfVVLm48jbG+APOpp54a8GvK2DYWx3lfK1euNP/mb/5mQO3pXDoyxtMY60vn0mvDWB+/58ycOdP86le/esltdI4cGeNpjPWlc+T4MlzjvK+f//znpt/vH1B/fvjDH5p+v9/s6elJP/aNb3zDLCkpMVOp1Hnb63pTRMY7zTgfg87dopWTkwP0zrRtbGxkzZo16W2cTicrV65k69atl2zH5/Nhs9kA2LZtG36/nyVLlqS3Wbp0KX6//5LtDMS2bdv69Q9g7dq17Nixg3g8fsF9YrEYO3fuPG+/NWvWXHF/5NLGyxi7HBs3bqSgoIBp06bxiU98gubm5ituU0bWWBzng6Fz6cgZL2PscuhcOnZcC+M3lUoRCoXSx3AhOkeOnPEyxi6HzpHXnuEa54O1bds2Vq5cidPpTD+2du1a6uvrqampGXS7OpeKyLVKwfkYY5omf/u3f8sNN9zA7NmzAWhsbASgsLCw37aFhYXp596tra2Nf/zHf+RTn/pU+rHGxkYKCgrO27agoOCi7QxUY2PjBfuXSCRobW294D6tra0kk8nLOi65cuNpjA3U7bffzq9//WteffVVvvOd7/DWW29xyy23EI1Gr6hdGTljdZwPhs6lI2M8jbGB0rl07LhWxu93vvMdwuEw991330W30TlyZIynMTZQOkdee4ZznA/Wxd4z9e3bYOhcKiLXKgXnY8xf/dVfsW/fPp544onznjMMo9/fTdM87zHorWt25513MnPmTL7yla9cso13tzNr1iy8Xi9er5fbb7/9svp+of6de/y1115Lt+v1evn1r3992cclQ2M8jrH3cv/993PnnXcye/Zs7r77bv74xz9y9OhRnn/++cvqn4weY3mcX4rOpaPHeBxj70Xn0rHjWhi/TzzxBI899hi//e1v0yGqzpGjx3gcY+9F58hrz3CP8/dysXF+qfdMA6FzqYiMJ1d2n49cVX/913/Ns88+y+bNm5kwYUL68aKiIqD3E+Li4uL0483Nzed94hsKhbjtttvwer089dRT2O32fu00NTWd97otLS3pdl544YV02Qu32z3gvhcVFZ33SXNzczM2m43c3Fz8fn+/FbULCwtxOp1YrdYL7vfu45KhMd7G2GAVFxdTXl7OsWPHBt2GjJyxPM7fy3XXXadz6Sgw3sbYYOlcOjpdC+P3t7/9LR//+Mf5r//6r36LPOocOTqMtzE2WDpHjm3DPc4H4kLj/GLvmWDg41XnUhEZTzTjfAwwTZO/+qu/4sknn+TVV1+lsrKy3/OVlZUUFRWxfv369GOxWIxNmzaxfPny9GPBYJA1a9bgcDh49tlncblc/dpZtmwZgUCAN998M/3YG2+8QSAQSLdTXl7OlClTmDJlCqWlpQM+hmXLlvXrH8BLL73Eddddh91ux+12p9udMmUKmZmZOBwOFi1adN5+69ev73dccuXG6xgbrLa2Nk6fPt3vYldGv2thnL8XnUtH1ngdY4Olc+nocq2M3yeeeIKHHnqIxx9/nDvvvLPfczpHjqzxOsYGS+fIselqjfOBuNA4X7ZsGZs3byYWi6W3e+mllygpKaGiomJA7epcKiLjynCvPipX7jOf+Yzp9/vNjRs3mg0NDemv7u7u9Dbf/OY3Tb/fbz755JPm/v37zQ996ENmcXGxGQwGTdM0zWAwaC5ZssScM2eOefz48X7tJBKJdDu33XabOXfuXHPbtm3mtm3bzDlz5ph33XXXe/bx4MGD5u7du827777bvOmmm8zdu3f3WyH75MmTpsfjMT/3uc+Zhw4dMn/605+adrvd/P3vf3/Jdn/zm9+Ydrvd/OlPf2oeOnTIfOSRR8yMjAyzpqYmvU1bW5u5e/du8/nnnzcB8ze/+Y25e/dus6GhYaA/4nFvPI+xUCiUbgswv/vd75q7d+82a2tr089//vOfN7du3WqeOnXK3LBhg7ls2TKztLQ0fewyNlwL49w0zfRjixYtMh944AFz9+7d5sGDBy/Zrs6lV8d4HmM6l45918L4ffzxx02bzWb+4Ac/6PfanZ2dl2xX58irYzyPMZ0jx4+rOc5ra2vN3bt3m1/96ldNr9ebHmOhUOii/evs7DQLCwvND33oQ+b+/fvNJ5980vT5fOa3v/3tftvpelNEpJeC8zEAuODXz3/+8/Q2qVTK/MpXvmIWFRWZTqfTXLFihbl///708xs2bLhoO6dOnUpv19bWZn74wx82MzMzzczMTPPDH/6w2dHR8Z59LC8vv2DbfW3cuNFcsGCB6XA4zIqKCvNHP/rRgI7/Bz/4gVleXm46HA5z4cKF5qZNm/o9//Of//yCr/2Vr3xlQO3L+B5jF+v3Rz/6UdM0TbO7u9tcs2aNmZ+fb9rtdnPixInmRz/6UbOuru4925bR5VoZ5xd6vry8/D3b1rl0+I3nMaZz6dh3LYzflStXXnIcXorOkcNvPI8xnSPHj6s5zj/60Y9ecJsNGzZcso/79u0zb7zxRtPpdJpFRUXmY489ZqZSqfc8Dl1vish4ZJjm2ytBiIiIiIiIiIiIiIiIapyLiIiIiIiIiIiIiPSl4FxEREREREREREREpA8F5yIiIiIiIiIiIiIifSg4FxERERERERERERHpQ8G5iIiIiIiIiIiIiEgfCs5FRERERERERERERPpQcC4iIiIiIiIiIiIi0oeCcxERERERERERERGRPhSci4iIiIiIiIiIiIj0oeBcREREZIQ89NBDGIaBYRjY7XYKCwtZvXo1P/vZz0ilUgNu5xe/+AVZWVnD11EREREREZFxRsG5iIiIyAi67bbbaGhooKamhj/+8Y/cfPPN/M3f/A133XUXiURipLsnIiIiIiIyLik4FxERERlBTqeToqIiSktLWbhwIX//93/PM888wx//+Ed+8YtfAPDd736XOXPmkJGRQVlZGZ/97Gfp6uoCYOPGjXzsYx8jEAikZ68/9thjAMRiMb70pS9RWlpKRkYGS5YsYePGjSNzoCIiIiIiImOIgnMRERGRUeaWW25h3rx5PPnkkwBYLBb+9V//lQMHDvAf//EfvPrqq3zpS18CYPny5fy//+//i8/no6GhgYaGBr7whS8A8LGPfYzXX3+d3/zmN+zbt48/+7M/47bbbuPYsWMjdmwiIiIiIiJjgWGapjnSnRAREREZjx566CE6Ozt5+umnz3vuz//8z9m3bx+HDh0677n/+q//4jOf+Qytra1Ab43zRx55hM7OzvQ2J06cYOrUqZw5c4aSkpL046tWreL666/n61//+pAfj4iIiIiIyLXCNtIdEBEREZHzmaaJYRgAbNiwga9//escOnSIYDBIIpGgp6eHcDhMRkbGBffftWsXpmkybdq0fo9Ho1Fyc3OHvf8iIiIiIiJjmYJzERERkVGourqayspKamtrueOOO/j0pz/NP/7jP5KTk8OWLVv4+Mc/Tjwev+j+qVQKq9XKzp07sVqt/Z7zer3D3X0REREREZExTcG5iIiIyCjz6quvsn//fj73uc+xY8cOEokE3/nOd7BYepen+d3vftdve4fDQTKZ7PfYggULSCaTNDc3c+ONN161vouIiIiIiFwLFJyLiIiIjKBoNEpjYyPJZJKmpibWrVvHN77xDe666y4+8pGPsH//fhKJBN/73ve4++67ef311/nxj3/cr42Kigq6urp45ZVXmDdvHh6Ph2nTpvHhD3+Yj3zkI3znO99hwYIFtLa28uqrrzJnzhzuuOOOETpiERERERGR0c8y0h0QERERGc/WrVtHcXExFRUV3HbbbWzYsIF//dd/5ZlnnsFqtTJ//ny++93v8s///M/Mnj2bX//613zjG9/o18by5cv59Kc/zf33309+fj7f+ta3APj5z3/ORz7yET7/+c8zffp07rnnHt544w3KyspG4lBFRERERETGDMM0TXOkOyEiIiIiIiIiIiIiMlpoxrmIiIiIiIiIiIiISB8KzkVERERERERERERE+lBwLiIiIiIiIiIiIiLSh4JzEREREREREREREZE+FJyLiIiIiIiIiIiIiPSh4FxEREREREREREREpA8F5yIiIiIiIiIiIiIifSg4FxERERERERERERHpQ8G5iIiIiIiIiIiIiEgfCs5FRERERERERERERPpQcC4iIiIiIiIiIiIi0oeCcxERERERERERERGRPv5/q1CCw+zG8KsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary Results:\n",
      "                    Model Accuracy Total Return (%) Sortino CVaR@95% (%) Max Drawdown (%)  Trades\n",
      "             Buy_and_Hold      N/A             4.10    2.52        -0.56            -1.79       1\n",
      "            Logistic_Base   0.6154             9.24    7.68        -0.46            -0.82      23\n",
      "        DecisionTree_Base   0.5846             1.83    0.61        -0.70            -2.81      27\n",
      "        RandomForest_Base   0.5077            -0.11   -0.67        -0.75            -3.45      14\n",
      "    GradientBoosting_Base   0.5385             1.64    0.48        -0.75            -2.67      18\n",
      "              Neural_Base   0.7077            11.54   10.36        -0.41            -1.06      23\n",
      "            AdaBoost_Base   0.5077             0.61   -0.20        -0.77            -2.67      16\n",
      "             XGBoost_Base   0.4769            -2.01   -1.88        -0.74            -3.72      24\n",
      "        Logistic_Enhanced   0.4308            -3.16   -2.49        -0.81            -4.27      33\n",
      "    DecisionTree_Enhanced   0.5077            -2.22   -1.91        -0.78            -3.65      29\n",
      "    RandomForest_Enhanced   0.5077            -0.54   -0.93        -0.75            -3.87      18\n",
      "GradientBoosting_Enhanced   0.5385             1.68    0.50        -0.75            -2.99      18\n",
      "          Neural_Enhanced   0.6769             9.10    8.21        -0.42            -1.06      24\n",
      "        AdaBoost_Enhanced   0.5231            -0.01   -0.59        -0.77            -2.85      16\n",
      "         XGBoost_Enhanced   0.4769            -1.36   -1.48        -0.76            -4.35      20\n"
     ]
    }
   ],
   "source": [
    "def main(currency_pair='EURUSD=X', start_date='2020-01-01', end_date='2020-12-31'):\n",
    "    \"\"\"Main analysis pipeline including buy-and-hold benchmark\"\"\"\n",
    "    # Download data\n",
    "    print(f\"Downloading data for {currency_pair}...\")\n",
    "    data = yf.download(currency_pair, start=start_date, end=end_date)\n",
    "    \n",
    "    # Prepare base and enhanced features\n",
    "    print(\"Preparing features...\")\n",
    "    base_features = prepare_features(data, enhanced=False)\n",
    "    enhanced_features = prepare_features(data, enhanced=True)\n",
    "    \n",
    "    # Clean and align data\n",
    "    base_features = base_features.dropna()\n",
    "    enhanced_features = enhanced_features.dropna()\n",
    "    common_index = base_features.index.intersection(enhanced_features.index)\n",
    "    \n",
    "    base_features = base_features.loc[common_index]\n",
    "    enhanced_features = enhanced_features.loc[common_index]\n",
    "    \n",
    "    # Prepare targets\n",
    "    target = (data['Close'].shift(-1) > data['Close']).loc[common_index].astype(int)[:-1]\n",
    "    base_features = base_features[:-1]\n",
    "    enhanced_features = enhanced_features[:-1]\n",
    "    test_prices = data.loc[common_index, 'Close'][:-1]\n",
    "    \n",
    "    # Split data\n",
    "    train_size = int(len(base_features) * 0.75)\n",
    "    X_train_base = base_features[:train_size]\n",
    "    X_test_base = base_features[train_size:]\n",
    "    X_train_enhanced = enhanced_features[:train_size]\n",
    "    X_test_enhanced = enhanced_features[train_size:]\n",
    "    y_train = target[:train_size]\n",
    "    y_test = target[train_size:]\n",
    "    test_prices = test_prices[train_size:]\n",
    "    \n",
    "    # Scale features\n",
    "    scaler_base = StandardScaler()\n",
    "    scaler_enhanced = StandardScaler()\n",
    "    \n",
    "    X_train_base_scaled = pd.DataFrame(\n",
    "        scaler_base.fit_transform(X_train_base),\n",
    "        index=X_train_base.index,\n",
    "        columns=X_train_base.columns\n",
    "    )\n",
    "    X_test_base_scaled = pd.DataFrame(\n",
    "        scaler_base.transform(X_test_base),\n",
    "        index=X_test_base.index,\n",
    "        columns=X_test_base.columns\n",
    "    )\n",
    "    \n",
    "    X_train_enhanced_scaled = pd.DataFrame(\n",
    "        scaler_enhanced.fit_transform(X_train_enhanced),\n",
    "        index=X_train_enhanced.index,\n",
    "        columns=X_train_enhanced.columns\n",
    "    )\n",
    "    X_test_enhanced_scaled = pd.DataFrame(\n",
    "        scaler_enhanced.transform(X_test_enhanced),\n",
    "        index=X_test_enhanced.index,\n",
    "        columns=X_test_enhanced.columns\n",
    "    )\n",
    "    \n",
    "    # Initialize baseline models\n",
    "    baseline_models = {\n",
    "        #'LightGBM_Base': LGBMClassifier(n_estimators=100, random_state=42),\n",
    "        'Logistic_Base': LogisticRegression(random_state=42, max_iter=1000),\n",
    "        'DecisionTree_Base': DecisionTreeClassifier(random_state=42),\n",
    "        'RandomForest_Base': RandomForestClassifier(n_estimators=100, random_state=42),\n",
    "        'GradientBoosting_Base': GradientBoostingClassifier(n_estimators=100, random_state=42),\n",
    "        'Neural_Base': MLPClassifier(hidden_layer_sizes=(50,), max_iter=1000, random_state=42),\n",
    "        'AdaBoost_Base': AdaBoostClassifier(n_estimators=50, random_state=42),\n",
    "        'XGBoost_Base': XGBClassifier(n_estimators=100, random_state=42)\n",
    "    }\n",
    "    \n",
    "    # Initialize enhanced models with optimized parameters\n",
    "    enhanced_models = {\n",
    "        'Logistic_Enhanced': LogisticRegression(\n",
    "            C=0.1,\n",
    "            max_iter=1000,\n",
    "            class_weight='balanced',\n",
    "            random_state=42\n",
    "        ),\n",
    "        'DecisionTree_Enhanced': DecisionTreeClassifier(\n",
    "            max_depth=6,\n",
    "            min_samples_split=20,\n",
    "            min_samples_leaf=10,\n",
    "            random_state=42\n",
    "        ),\n",
    "        'RandomForest_Enhanced': RandomForestClassifier(\n",
    "            n_estimators=200,\n",
    "            max_depth=10,\n",
    "            min_samples_split=20,\n",
    "            min_samples_leaf=10,\n",
    "            max_features='sqrt',\n",
    "            random_state=42\n",
    "        ),\n",
    "        'GradientBoosting_Enhanced': GradientBoostingClassifier(\n",
    "            n_estimators=200,\n",
    "            learning_rate=0.05,\n",
    "            max_depth=4,\n",
    "            min_samples_split=20,\n",
    "            subsample=0.8,\n",
    "            random_state=42\n",
    "        ),\n",
    "        'Neural_Enhanced': MLPClassifier(\n",
    "            hidden_layer_sizes=(100, 50),\n",
    "            activation='relu',\n",
    "            solver='adam',\n",
    "            alpha=0.001,\n",
    "            batch_size=32,\n",
    "            learning_rate='adaptive',\n",
    "            max_iter=1000,\n",
    "            random_state=42\n",
    "        ),\n",
    "        'AdaBoost_Enhanced': AdaBoostClassifier(\n",
    "            n_estimators=100,\n",
    "            learning_rate=0.05,\n",
    "            random_state=42\n",
    "        ),\n",
    "        'XGBoost_Enhanced': XGBClassifier(\n",
    "            n_estimators=200,\n",
    "            learning_rate=0.05,\n",
    "            max_depth=5,\n",
    "            min_child_weight=2,\n",
    "            gamma=0.1,\n",
    "            subsample=0.8,\n",
    "            colsample_bytree=0.8,\n",
    "            random_state=42\n",
    "        )\n",
    "    }\n",
    "    # Start the analysis pipeline\n",
    "    results = {}\n",
    "\n",
    "    # Add buy-and-hold benchmark\n",
    "    print(\"\\nCalculating Buy-and-Hold benchmark...\")\n",
    "    bh_portfolio_values, bh_metrics = backtest_buy_and_hold(test_prices)\n",
    "    results['Buy_and_Hold'] = {\n",
    "        'Accuracy': None,  # Not applicable for buy-and-hold\n",
    "        'Portfolio_Values': bh_portfolio_values,\n",
    "        **bh_metrics\n",
    "    }\n",
    "    \n",
    "    print(f\"Total Return: {bh_metrics['Total_Return']:.2f}%\")\n",
    "    print(f\"Sortino: {bh_metrics['Sortino']:.2f}\")\n",
    "    print(f\"CVaR@95%: {bh_metrics['CVaR_95']*100:.2f}%\")\n",
    "    print(f\"Max Drawdown: {bh_metrics['MaxDrawdown']*100:.2f}%\")\n",
    "    print(f\"Number of Trades: {bh_metrics['Trades']}\")\n",
    "    \n",
    "    print(\"\\nRunning backtests with transaction costs of 5 pips per round trip (2 spread + 3 commission)\")\n",
    "    \n",
    "    # Train and evaluate baseline models\n",
    "    for name, model in baseline_models.items():\n",
    "        print(f\"\\nTraining {name}...\")\n",
    "        model.fit(X_train_base_scaled, y_train)\n",
    "        test_pred = model.predict(X_test_base_scaled)\n",
    "        accuracy = (test_pred == y_test).mean()\n",
    "        portfolio_values, metrics = backtest_strategy(test_pred, test_prices)\n",
    "        \n",
    "        results[name] = {\n",
    "            'Accuracy': accuracy,\n",
    "            'Portfolio_Values': portfolio_values,\n",
    "            **metrics\n",
    "        }\n",
    "        \n",
    "        print(f\"Accuracy: {accuracy:.4f}\")\n",
    "        print(f\"Total Return: {metrics['Total_Return']:.2f}%\")\n",
    "        print(f\"Sortino: {metrics['Sortino']:.2f}\")\n",
    "        print(f\"CVaR@95%: {metrics['CVaR_95']*100:.2f}%\")\n",
    "        print(f\"Max Drawdown: {metrics['MaxDrawdown']*100:.2f}%\")\n",
    "        print(f\"Number of Trades: {metrics['Trades']}\")\n",
    "    \n",
    "    # Train and evaluate enhanced models\n",
    "    for name, model in enhanced_models.items():\n",
    "        print(f\"\\nTraining {name}...\")\n",
    "        model.fit(X_train_enhanced_scaled, y_train)\n",
    "        test_pred = model.predict(X_test_enhanced_scaled)\n",
    "        accuracy = (test_pred == y_test).mean()\n",
    "        portfolio_values, metrics = backtest_strategy(test_pred, test_prices)\n",
    "        \n",
    "        results[name] = {\n",
    "            'Accuracy': accuracy,\n",
    "            'Portfolio_Values': portfolio_values,\n",
    "            **metrics\n",
    "        }\n",
    "        \n",
    "        print(f\"Accuracy: {accuracy:.4f}\")\n",
    "        print(f\"Total Return: {metrics['Total_Return']:.2f}%\")\n",
    "        print(f\"Sortino: {metrics['Sortino']:.2f}\")\n",
    "        print(f\"CVaR@95%: {metrics['CVaR_95']*100:.2f}%\")\n",
    "        print(f\"Max Drawdown: {metrics['MaxDrawdown']*100:.2f}%\")\n",
    "        print(f\"Number of Trades: {metrics['Trades']}\")\n",
    "    \n",
    "    return results\n",
    "\n",
    "# Run analysis and create visualization\n",
    "if __name__ == \"__main__\":\n",
    "    results = main()\n",
    "    \n",
    "    # Create performance comparison plot\n",
    "    plt.figure(figsize=(15, 8))\n",
    "    \n",
    "    # Plot buy-and-hold benchmark\n",
    "    plt.plot(results['Buy_and_Hold']['Portfolio_Values'],\n",
    "             label='Buy and Hold',\n",
    "             color='black',\n",
    "             linewidth=2)\n",
    "    \n",
    "    # Plot baseline models\n",
    "    for name, result in results.items():\n",
    "        if 'Base' in name and name != 'Buy_and_Hold':\n",
    "            plt.plot(result['Portfolio_Values'], \n",
    "                    label=name, \n",
    "                    linestyle='--',\n",
    "                    alpha=0.7)\n",
    "    \n",
    "    # Plot enhanced models\n",
    "    for name, result in results.items():\n",
    "        if 'Enhanced' in name:\n",
    "            plt.plot(result['Portfolio_Values'], \n",
    "                    label=name,\n",
    "                    alpha=0.7)\n",
    "    \n",
    "    plt.title('Model Performance Comparison\\n(Including 5 pip transaction costs per round trip)')\n",
    "    plt.xlabel('Date')\n",
    "    plt.ylabel('Portfolio Value ($)')\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # Print summary table\n",
    "    summary_data = []\n",
    "    for name, result in results.items():\n",
    "        summary_data.append({\n",
    "            'Model': name,\n",
    "            'Accuracy': f\"{result['Accuracy']:.4f}\" if result['Accuracy'] is not None else \"N/A\",\n",
    "            'Total Return (%)': f\"{result['Total_Return']:.2f}\",\n",
    "            'Sortino': f\"{result['Sortino']:.2f}\",\n",
    "            'CVaR@95% (%)': f\"{result['CVaR_95']*100:.2f}\",\n",
    "            'Max Drawdown (%)': f\"{result['MaxDrawdown']*100:.2f}\",\n",
    "            'Trades': result['Trades']\n",
    "        })\n",
    "    \n",
    "    summary_df = pd.DataFrame(summary_data)\n",
    "    print(\"\\nSummary Results:\")\n",
    "    print(summary_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "653e5548-1259-4b36-a7a9-57d210e39f10",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c973e4e5-69f5-45f3-a144-b43e7eeada80",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def calculate_test_periods(train_start, train_end):\n",
    "    \"\"\"Calculate training and testing periods based on input dates\"\"\"\n",
    "    from datetime import datetime, timedelta\n",
    "    \n",
    "    # Convert string dates to datetime\n",
    "    start_dt = datetime.strptime(train_start, '%Y-%m-%d')\n",
    "    end_dt = datetime.strptime(train_end, '%Y-%m-%d')\n",
    "    \n",
    "    # Calculate total training period length\n",
    "    total_days = (end_dt - start_dt).days\n",
    "    \n",
    "    # Calculate test set length (25% of total period)\n",
    "    test_length = int(total_days * 0.25)\n",
    "    \n",
    "    # Calculate future period dates\n",
    "    next_period_start = end_dt + timedelta(days=1)\n",
    "    next_period_end = next_period_start + timedelta(days=test_length)\n",
    "    \n",
    "    print(f\"\\nPeriod Calculations:\")\n",
    "    print(f\"Training period total days: {total_days}\")\n",
    "    print(f\"Test set length (25%): {test_length} days\")\n",
    "    \n",
    "    return {\n",
    "        'train_period': {\n",
    "            'start': train_start,\n",
    "            'end': train_end\n",
    "        },\n",
    "        'future_period': {\n",
    "            'start': next_period_start.strftime('%Y-%m-%d'),\n",
    "            'end': next_period_end.strftime('%Y-%m-%d')\n",
    "        }\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2a80aa45-088f-476c-9711-a7c5222291cc",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def main_enhanced_flexible(currency_pair='EURUSD=X', train_start='2019-01-01', train_end='2019-12-31'):\n",
    "    \"\"\"Enhanced main function with automatic period calculation and data validation\"\"\"\n",
    "    \n",
    "    # Calculate periods\n",
    "    periods = calculate_test_periods(train_start, train_end)\n",
    "    print(\"\\nAnalysis Periods:\")\n",
    "    print(f\"Training Period: {periods['train_period']['start']} to {periods['train_period']['end']}\")\n",
    "    print(f\"Future Testing Period: {periods['future_period']['start']} to {periods['future_period']['end']}\")\n",
    "    \n",
    "    # Download training data\n",
    "    print(f\"\\nDownloading training data for {currency_pair}...\")\n",
    "    train_data = yf.download(currency_pair, \n",
    "                            start=periods['train_period']['start'], \n",
    "                            end=periods['train_period']['end'])\n",
    "    print(f\"Downloaded training data shape: {train_data.shape}\")\n",
    "    \n",
    "    if len(train_data) == 0:\n",
    "        raise ValueError(\"No training data downloaded. Please check the date range and currency pair.\")\n",
    "    \n",
    "    # Download future data\n",
    "    print(f\"\\nDownloading future test data for {currency_pair}...\")\n",
    "    future_data = yf.download(currency_pair, \n",
    "                             start=periods['future_period']['start'], \n",
    "                             end=periods['future_period']['end'])\n",
    "    print(f\"Downloaded future data shape: {future_data.shape}\")\n",
    "    \n",
    "    if len(future_data) == 0:\n",
    "        raise ValueError(\"No future data downloaded. Please check the date range.\")\n",
    "    \n",
    "    # Prepare features for training period\n",
    "    print(\"\\nPreparing features...\")\n",
    "    base_features = prepare_features(train_data, enhanced=False)\n",
    "    enhanced_features = prepare_features(train_data, enhanced=True)\n",
    "    \n",
    "    print(f\"Base features shape before cleaning: {base_features.shape}\")\n",
    "    print(f\"Enhanced features shape before cleaning: {enhanced_features.shape}\")\n",
    "    \n",
    "    # Clean and align training data\n",
    "    base_features = base_features.dropna()\n",
    "    enhanced_features = enhanced_features.dropna()\n",
    "    common_index = base_features.index.intersection(enhanced_features.index)\n",
    "    \n",
    "    base_features = base_features.loc[common_index]\n",
    "    enhanced_features = enhanced_features.loc[common_index]\n",
    "    \n",
    "    print(f\"Features shape after cleaning: {base_features.shape}\")\n",
    "    \n",
    "    if len(base_features) == 0:\n",
    "        raise ValueError(\"No valid features after cleaning. Please check the data preparation process.\")\n",
    "    \n",
    "    # Continue with the rest of your code...def main_enhanced_flexible(currency_pair='EURUSD=X', train_start='2019-01-01', train_end='2019-12-31'):\n",
    "    \"\"\"Enhanced main function with automatic period calculation\"\"\"\n",
    "    \n",
    "    # Calculate periods\n",
    "    periods = calculate_test_periods(train_start, train_end)\n",
    "    print(\"\\nAnalysis Periods:\")\n",
    "    print(f\"Training Period: {periods['train_period']['start']} to {periods['train_period']['end']}\")\n",
    "    print(f\"Future Testing Period: {periods['future_period']['start']} to {periods['future_period']['end']}\")\n",
    "    \n",
    "    # Download training data\n",
    "    print(f\"\\nDownloading training data for {currency_pair}...\")\n",
    "    train_data = yf.download(currency_pair, \n",
    "                            start=periods['train_period']['start'], \n",
    "                            end=periods['train_period']['end'])\n",
    "    \n",
    "    # Download future data\n",
    "    print(f\"\\nDownloading future test data for {currency_pair}...\")\n",
    "    future_data = yf.download(currency_pair, \n",
    "                             start=periods['future_period']['start'], \n",
    "                             end=periods['future_period']['end'])\n",
    "    \n",
    "    # Prepare features for training period\n",
    "    print(\"\\nPreparing features...\")\n",
    "    base_features = prepare_features(train_data, enhanced=False)\n",
    "    enhanced_features = prepare_features(train_data, enhanced=True)\n",
    "    \n",
    "    # Clean and align training data\n",
    "    base_features = base_features.dropna()\n",
    "    enhanced_features = enhanced_features.dropna()\n",
    "    common_index = base_features.index.intersection(enhanced_features.index)\n",
    "    \n",
    "    base_features = base_features.loc[common_index]\n",
    "    enhanced_features = enhanced_features.loc[common_index]\n",
    "    \n",
    "    # Prepare target for training period\n",
    "    target = (train_data['Close'].shift(-1) > train_data['Close']).loc[common_index].astype(int)[:-1]\n",
    "    base_features = base_features[:-1]\n",
    "    enhanced_features = enhanced_features[:-1]\n",
    "    \n",
    "    # Split training data (75/25)\n",
    "    train_size = int(len(base_features) * 0.75)\n",
    "    \n",
    "    # Training data\n",
    "    X_train_base = base_features[:train_size]\n",
    "    X_train_enhanced = enhanced_features[:train_size]\n",
    "    y_train = target[:train_size]\n",
    "    \n",
    "    # Test data from training period\n",
    "    X_test_base = base_features[train_size:]\n",
    "    X_test_enhanced = enhanced_features[train_size:]\n",
    "    y_test = target[train_size:]\n",
    "    test_prices = train_data['Close'].loc[common_index][train_size:-1]\n",
    "    \n",
    "    # Scale features\n",
    "    scaler_base = StandardScaler()\n",
    "    scaler_enhanced = StandardScaler()\n",
    "    \n",
    "    X_train_base_scaled = pd.DataFrame(\n",
    "        scaler_base.fit_transform(X_train_base),\n",
    "        index=X_train_base.index,\n",
    "        columns=X_train_base.columns\n",
    "    )\n",
    "    \n",
    "    X_train_enhanced_scaled = pd.DataFrame(\n",
    "        scaler_enhanced.fit_transform(X_train_enhanced),\n",
    "        index=X_train_enhanced.index,\n",
    "        columns=X_train_enhanced.columns\n",
    "    )\n",
    "    \n",
    "    X_test_base_scaled = pd.DataFrame(\n",
    "        scaler_base.transform(X_test_base),\n",
    "        index=X_test_base.index,\n",
    "        columns=X_test_base.columns\n",
    "    )\n",
    "    \n",
    "    X_test_enhanced_scaled = pd.DataFrame(\n",
    "        scaler_enhanced.transform(X_test_enhanced),\n",
    "        index=X_test_enhanced.index,\n",
    "        columns=X_test_enhanced.columns\n",
    "    )\n",
    "    \n",
    "    # Prepare future period data\n",
    "    base_features_future = prepare_features(future_data, enhanced=False)\n",
    "    enhanced_features_future = prepare_features(future_data, enhanced=True)\n",
    "    \n",
    "    # Clean and align future data\n",
    "    base_features_future = base_features_future.dropna()\n",
    "    enhanced_features_future = enhanced_features_future.dropna()\n",
    "    common_index_future = base_features_future.index.intersection(enhanced_features_future.index)\n",
    "    \n",
    "    base_features_future = base_features_future.loc[common_index_future][:-1]\n",
    "    enhanced_features_future = enhanced_features_future.loc[common_index_future][:-1]\n",
    "    target_future = (future_data['Close'].shift(-1) > future_data['Close']).loc[common_index_future].astype(int)[:-1]\n",
    "    future_prices = future_data['Close'].loc[common_index_future][:-1]\n",
    "    \n",
    "    X_future_base_scaled = pd.DataFrame(\n",
    "        scaler_base.transform(base_features_future),\n",
    "        index=base_features_future.index,\n",
    "        columns=base_features_future.columns\n",
    "    )\n",
    "    \n",
    "    X_future_enhanced_scaled = pd.DataFrame(\n",
    "        scaler_enhanced.transform(enhanced_features_future),\n",
    "        index=enhanced_features_future.index,\n",
    "        columns=enhanced_features_future.columns\n",
    "    )\n",
    "    \n",
    "    # Initialize models\n",
    "    baseline_models = {\n",
    "        'Logistic_Base': LogisticRegression(random_state=42, max_iter=1000), \n",
    "        'DecisionTree_Base': DecisionTreeClassifier(random_state=42),\n",
    "        'RandomForest_Base': RandomForestClassifier(n_estimators=100, random_state=42),\n",
    "        'GradientBoosting_Base': GradientBoostingClassifier(n_estimators=100, random_state=42),\n",
    "        'Neural_Base': MLPClassifier(hidden_layer_sizes=(50,), max_iter=1000, random_state=42),\n",
    "        'AdaBoost_Base': AdaBoostClassifier(n_estimators=50, random_state=42),\n",
    "        'XGBoost_Base': XGBClassifier(n_estimators=100, random_state=42)\n",
    "    }\n",
    "    \n",
    "    enhanced_models = {\n",
    "        'Logistic_Enhanced': LogisticRegression(random_state=42, max_iter=1000,class_weight='balanced',C=0.1),\n",
    "        'DecisionTree_Enhanced': DecisionTreeClassifier(random_state=42,max_depth=6,min_samples_split=20,min_samples_leaf=10),\n",
    "        'RandomForest_Enhanced': RandomForestClassifier(n_estimators=200, random_state=42,min_samples_split=20,min_samples_leaf=10,max_features='sqrt'),\n",
    "        'GradientBoosting_Enhanced': GradientBoostingClassifier(n_estimators=200, random_state=42,learning_rate=0.5,max_depth=4,min_samples_split=20,subsample=0.8),\n",
    "        'Neural_Enhanced': MLPClassifier(hidden_layer_sizes=(100,50), max_iter=1000, random_state=42,activation='relu',solver='adam',alpha=0.001,learning_rate='adaptive'),\n",
    "        'AdaBoost_Enhanced': AdaBoostClassifier(n_estimators=100, random_state=42,learning_rate=0.05),\n",
    "        'XGBoost_Enhanced': XGBClassifier(n_estimators=200, random_state=42,learning_rate=0.05,max_depth=5,min_child_weight=2,gamma=0.01,subsample=0.08,colsample_bytrees=0.08)\n",
    "    }\n",
    "    \n",
    "    # Train models and evaluate\n",
    "    results = {}\n",
    "    \n",
    "    # Calculate buy-and-hold for both periods\n",
    "    print(\"\\nCalculating Buy-and-Hold benchmarks...\")\n",
    "    bh_test_values, bh_test_metrics = backtest_buy_and_hold(test_prices)\n",
    "    bh_future_values, bh_future_metrics = backtest_buy_and_hold(future_prices)\n",
    "    \n",
    "    results['Buy_and_Hold'] = {\n",
    "        'test_period': {\n",
    "            'Accuracy': None,\n",
    "            'Portfolio_Values': bh_test_values,\n",
    "            **bh_test_metrics\n",
    "        },\n",
    "        'future_period': {\n",
    "            'Accuracy': None,\n",
    "            'Portfolio_Values': bh_future_values,\n",
    "            **bh_future_metrics\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    print(\"\\nBuy-and-Hold Test Period:\")\n",
    "    print(f\"Total Return: {bh_test_metrics['Total_Return']:.2f}%\")\n",
    "    print(f\"Sortino: {bh_test_metrics['Sortino']:.2f}\")\n",
    "    print(f\"Max Drawdown: {bh_test_metrics['MaxDrawdown']*100:.2f}%\")\n",
    "    \n",
    "    print(\"\\nBuy-and-Hold Future Period:\")\n",
    "    print(f\"Total Return: {bh_future_metrics['Total_Return']:.2f}%\")\n",
    "    print(f\"Sortino: {bh_future_metrics['Sortino']:.2f}\")\n",
    "    print(f\"Max Drawdown: {bh_future_metrics['MaxDrawdown']*100:.2f}%\")\n",
    "    \n",
    "    # Train and evaluate all models\n",
    "    all_models = {**baseline_models, **enhanced_models}\n",
    "    \n",
    "    for name, model in all_models.items():\n",
    "        print(f\"\\nTraining and evaluating {name}...\")\n",
    "        \n",
    "        # Select appropriate feature set\n",
    "        if 'Enhanced' in name:\n",
    "            X_train = X_train_enhanced_scaled\n",
    "            X_test = X_test_enhanced_scaled\n",
    "            X_future = X_future_enhanced_scaled\n",
    "        else:\n",
    "            X_train = X_train_base_scaled\n",
    "            X_test = X_test_base_scaled\n",
    "            X_future = X_future_base_scaled\n",
    "        \n",
    "        # Train model once\n",
    "        model.fit(X_train, y_train)\n",
    "        \n",
    "        # Test period predictions and evaluation\n",
    "        pred_test = model.predict(X_test)\n",
    "        acc_test = (pred_test == y_test).mean()\n",
    "        portfolio_values_test, metrics_test = backtest_strategy(pred_test, test_prices)\n",
    "        \n",
    "        # Future period predictions and evaluation\n",
    "        pred_future = model.predict(X_future)\n",
    "        acc_future = (pred_future == target_future).mean()\n",
    "        portfolio_values_future, metrics_future = backtest_strategy(pred_future, future_prices)\n",
    "        \n",
    "        results[name] = {\n",
    "            'test_period': {\n",
    "                'Accuracy': acc_test,\n",
    "                'Portfolio_Values': portfolio_values_test,\n",
    "                **metrics_test\n",
    "            },\n",
    "            'future_period': {\n",
    "                'Accuracy': acc_future,\n",
    "                'Portfolio_Values': portfolio_values_future,\n",
    "                **metrics_future\n",
    "            }\n",
    "        }\n",
    "        \n",
    "        print(f\"\\nTest Period Results:\")\n",
    "        print(f\"Accuracy: {acc_test:.4f}\")\n",
    "        print(f\"Total Return: {metrics_test['Total_Return']:.2f}%\")\n",
    "        print(f\"Sortino: {metrics_test['Sortino']:.2f}\")\n",
    "        print(f\"Max Drawdown: {metrics_test['MaxDrawdown']*100:.2f}%\")\n",
    "        print(f\"Number of Trades: {metrics_test['Trades']}\")\n",
    "        \n",
    "        print(f\"\\nFuture Period Results:\")\n",
    "        print(f\"Accuracy: {acc_future:.4f}\")\n",
    "        print(f\"Total Return: {metrics_future['Total_Return']:.2f}%\")\n",
    "        print(f\"Sortino: {metrics_future['Sortino']:.2f}\")\n",
    "        print(f\"Max Drawdown: {metrics_future['MaxDrawdown']*100:.2f}%\")\n",
    "        print(f\"Number of Trades: {metrics_future['Trades']}\")\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "87963d58-47c8-42f4-8880-ecabb7a62da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shorten_model_name(name):\n",
    "    \"\"\"Create shorter version of model names for Excel sheets\"\"\"\n",
    "    replacements = {\n",
    "        'GradientBoosting': 'GB',\n",
    "        'RandomForest': 'RF',\n",
    "        'DecisionTree': 'DT',\n",
    "        'Logistic': 'L',\n",
    "        'Neural': 'NN',\n",
    "        'AdaBoost': 'AB',\n",
    "        'XGBoost': 'XG',\n",
    "        'Enhanced': 'E',\n",
    "        'Base': 'B'\n",
    "    }\n",
    "    \n",
    "    short_name = name\n",
    "    for old, new in replacements.items():\n",
    "        short_name = short_name.replace(old, new)\n",
    "    \n",
    "    return short_name\n",
    "\n",
    "def export_results_to_excel(results, filename='trading_results_a.xlsx'):\n",
    "    \"\"\"Export results to Excel with multiple sheets\"\"\"\n",
    "    \n",
    "    # Create Excel writer\n",
    "    with pd.ExcelWriter(filename) as writer:\n",
    "        \n",
    "        # Create summary DataFrame for both periods\n",
    "        summary_test = []\n",
    "        summary_future = []\n",
    "        \n",
    "        for model_name, model_results in results.items():\n",
    "            # Test period results\n",
    "            row_test = {\n",
    "                'Model': model_name,\n",
    "                'Accuracy': model_results['test_period'].get('Accuracy'),\n",
    "                'Total Return (%)': model_results['test_period']['Total_Return'],\n",
    "                'Sortino': model_results['test_period']['Sortino'],\n",
    "                'Max Drawdown (%)': model_results['test_period']['MaxDrawdown'] * 100,\n",
    "                'CVaR 95% (%)': model_results['test_period']['CVaR_95'] * 100,\n",
    "                'Trades': model_results['test_period']['Trades']\n",
    "            }\n",
    "            summary_test.append(row_test)\n",
    "            \n",
    "            # Future period results\n",
    "            row_future = {\n",
    "                'Model': model_name,\n",
    "                'Accuracy': model_results['future_period'].get('Accuracy'),\n",
    "                'Total Return (%)': model_results['future_period']['Total_Return'],\n",
    "                'Sortino': model_results['future_period']['Sortino'],\n",
    "                'Max Drawdown (%)': model_results['future_period']['MaxDrawdown'] * 100,\n",
    "                'CVaR 95% (%)': model_results['future_period']['CVaR_95'] * 100,\n",
    "                'Trades': model_results['future_period']['Trades']\n",
    "            }\n",
    "            summary_future.append(row_future)\n",
    "        \n",
    "        # Convert to DataFrames\n",
    "        df_test = pd.DataFrame(summary_test)\n",
    "        df_future = pd.DataFrame(summary_future)\n",
    "        \n",
    "        # Export summary sheets\n",
    "        df_test.to_excel(writer, sheet_name='Test_Summary', index=False)\n",
    "        df_future.to_excel(writer, sheet_name='Future_Summary', index=False)\n",
    "        \n",
    "        # Export detailed portfolio values\n",
    "        for model_name, model_results in results.items():\n",
    "            # Get shortened model name\n",
    "            short_name = shorten_model_name(model_name)\n",
    "            \n",
    "            portfolio_values_test = pd.DataFrame({\n",
    "                'Date': model_results['test_period']['Portfolio_Values'].index,\n",
    "                'Portfolio_Value': model_results['test_period']['Portfolio_Values'].values\n",
    "            })\n",
    "            portfolio_values_future = pd.DataFrame({\n",
    "                'Date': model_results['future_period']['Portfolio_Values'].index,\n",
    "                'Portfolio_Value': model_results['future_period']['Portfolio_Values'].values\n",
    "            })\n",
    "            \n",
    "            # Use shortened names for sheets\n",
    "            portfolio_values_test.to_excel(writer, sheet_name=f'{short_name}_Test', index=False)\n",
    "            portfolio_values_future.to_excel(writer, sheet_name=f'{short_name}_Fut', index=False)\n",
    "    \n",
    "    print(f\"\\nResults exported to {filename}\")\n",
    "    return df_test, df_future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "edb3e9a2-033d-407d-8d6b-532e20140155",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (259, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (259, 9)\n",
      "Enhanced features shape before cleaning: (259, 9)\n",
      "Features shape after cleaning: (258, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 1.45%\n",
      "Sortino: 0.38\n",
      "Max Drawdown: -1.45%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -0.73%\n",
      "Sortino: -0.64\n",
      "Max Drawdown: -6.50%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 5.87%\n",
      "Sortino: 6.59\n",
      "Max Drawdown: -0.84%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8413\n",
      "Total Return: 22.32%\n",
      "Sortino: 16.45\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -1.59%\n",
      "Sortino: -2.46\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 4.69%\n",
      "Sortino: 2.09\n",
      "Max Drawdown: -2.57%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 0.80%\n",
      "Sortino: -0.15\n",
      "Max Drawdown: -1.65%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 2.65%\n",
      "Sortino: 0.65\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 2.00%\n",
      "Sortino: 0.98\n",
      "Max Drawdown: -1.42%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 11.37%\n",
      "Sortino: 7.31\n",
      "Max Drawdown: -1.95%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 6.77%\n",
      "Sortino: 7.92\n",
      "Max Drawdown: -0.84%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7619\n",
      "Total Return: 11.25%\n",
      "Sortino: 3.16\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 3.15%\n",
      "Sortino: 2.54\n",
      "Max Drawdown: -0.79%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 5.68%\n",
      "Sortino: 1.98\n",
      "Max Drawdown: -3.25%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 0.72%\n",
      "Sortino: -0.22\n",
      "Max Drawdown: -2.64%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 3.11%\n",
      "Sortino: 0.83\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 2.34%\n",
      "Sortino: 1.33\n",
      "Max Drawdown: -1.53%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 3.11%\n",
      "Sortino: 0.87\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 12\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 1.54%\n",
      "Sortino: 0.58\n",
      "Max Drawdown: -1.76%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -1.70%\n",
      "Sortino: -0.88\n",
      "Max Drawdown: -7.73%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.41%\n",
      "Sortino: -1.36\n",
      "Max Drawdown: -2.80%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 4.23%\n",
      "Sortino: 1.29\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 3.54%\n",
      "Sortino: 2.77\n",
      "Max Drawdown: -0.79%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 6.33%\n",
      "Sortino: 2.02\n",
      "Max Drawdown: -3.40%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 5.59%\n",
      "Sortino: 5.67\n",
      "Max Drawdown: -0.55%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7143\n",
      "Total Return: 8.09%\n",
      "Sortino: 2.42\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 0.92%\n",
      "Sortino: -0.04\n",
      "Max Drawdown: -1.36%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 7.41%\n",
      "Sortino: 2.21\n",
      "Max Drawdown: -4.72%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.3538\n",
      "Total Return: -2.57%\n",
      "Sortino: -3.69\n",
      "Max Drawdown: -3.63%\n",
      "Number of Trades: 3\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -4.63%\n",
      "Sortino: -2.00\n",
      "Max Drawdown: -7.55%\n",
      "Number of Trades: 9\n",
      "\n",
      "Results exported to trading_results_a.xlsx\n"
     ]
    }
   ],
   "source": [
    "results = main_enhanced_flexible(\n",
    "    currency_pair='EURUSD=X',\n",
    "    train_start='2019-01-01',\n",
    "    train_end='2019-12-31'\n",
    ")\n",
    "\n",
    "df_test, df_future = export_results_to_excel(results, filename='trading_results_a.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6e0156e-58e7-427b-ab65-6a9d9c644d1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f777fe2-400e-4524-aeda-74cb35c06dab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b25d535-73f9-4ff1-b7e8-96fa3012f72a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "05a88e8c-3dc1-4606-9c3a-24b443e36304",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -2.37%\n",
      "Sortino: -1.96\n",
      "Max Drawdown: -3.20%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -2.02%\n",
      "Sortino: -1.71\n",
      "Max Drawdown: -3.09%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 10.81%\n",
      "Sortino: 7.40\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 9.82%\n",
      "Sortino: 8.38\n",
      "Max Drawdown: -1.11%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 6.06%\n",
      "Sortino: 3.13\n",
      "Max Drawdown: -1.06%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 0.57%\n",
      "Sortino: -0.21\n",
      "Max Drawdown: -2.41%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 2.77%\n",
      "Sortino: 1.13\n",
      "Max Drawdown: -2.48%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -3.42%\n",
      "Sortino: -2.59\n",
      "Max Drawdown: -7.02%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: -0.05%\n",
      "Sortino: -0.51\n",
      "Max Drawdown: -2.43%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 0.55%\n",
      "Sortino: -0.22\n",
      "Max Drawdown: -3.81%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 11.01%\n",
      "Sortino: 6.77\n",
      "Max Drawdown: -1.05%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7619\n",
      "Total Return: 13.37%\n",
      "Sortino: 12.27\n",
      "Max Drawdown: -0.62%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -1.33%\n",
      "Sortino: -1.37\n",
      "Max Drawdown: -3.02%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 1.22%\n",
      "Sortino: 0.23\n",
      "Max Drawdown: -2.50%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -1.90%\n",
      "Sortino: -1.61\n",
      "Max Drawdown: -2.74%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 2.05%\n",
      "Sortino: 0.69\n",
      "Max Drawdown: -2.46%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: -2.22%\n",
      "Sortino: -1.76\n",
      "Max Drawdown: -4.28%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -0.73%\n",
      "Sortino: -1.03\n",
      "Max Drawdown: -3.85%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: 1.44%\n",
      "Sortino: 0.35\n",
      "Max Drawdown: -1.99%\n",
      "Number of Trades: 5\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 1.91%\n",
      "Sortino: 0.71\n",
      "Max Drawdown: -2.07%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -2.42%\n",
      "Sortino: -1.96\n",
      "Max Drawdown: -3.20%\n",
      "Number of Trades: 1\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -2.07%\n",
      "Sortino: -1.71\n",
      "Max Drawdown: -3.09%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 2.33%\n",
      "Sortino: 0.79\n",
      "Max Drawdown: -2.10%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -2.38%\n",
      "Sortino: -2.05\n",
      "Max Drawdown: -5.97%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 10.88%\n",
      "Sortino: 7.51\n",
      "Max Drawdown: -1.12%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 2.96%\n",
      "Sortino: 1.40\n",
      "Max Drawdown: -2.33%\n",
      "Number of Trades: 12\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -2.42%\n",
      "Sortino: -1.96\n",
      "Max Drawdown: -3.20%\n",
      "Number of Trades: 1\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -2.07%\n",
      "Sortino: -1.71\n",
      "Max Drawdown: -3.09%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -2.15%\n",
      "Sortino: -1.83\n",
      "Max Drawdown: -4.25%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -3.67%\n",
      "Sortino: -2.60\n",
      "Max Drawdown: -4.58%\n",
      "Number of Trades: 11\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (259, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (259, 9)\n",
      "Enhanced features shape before cleaning: (259, 9)\n",
      "Features shape after cleaning: (258, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 1.45%\n",
      "Sortino: 0.38\n",
      "Max Drawdown: -1.45%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -0.73%\n",
      "Sortino: -0.64\n",
      "Max Drawdown: -6.50%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 5.87%\n",
      "Sortino: 6.59\n",
      "Max Drawdown: -0.84%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8413\n",
      "Total Return: 22.32%\n",
      "Sortino: 16.45\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -1.59%\n",
      "Sortino: -2.46\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 4.69%\n",
      "Sortino: 2.09\n",
      "Max Drawdown: -2.57%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 0.80%\n",
      "Sortino: -0.15\n",
      "Max Drawdown: -1.65%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 2.65%\n",
      "Sortino: 0.65\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 2.00%\n",
      "Sortino: 0.98\n",
      "Max Drawdown: -1.42%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 11.37%\n",
      "Sortino: 7.31\n",
      "Max Drawdown: -1.95%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 6.77%\n",
      "Sortino: 7.92\n",
      "Max Drawdown: -0.84%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7619\n",
      "Total Return: 11.25%\n",
      "Sortino: 3.16\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 3.15%\n",
      "Sortino: 2.54\n",
      "Max Drawdown: -0.79%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 5.68%\n",
      "Sortino: 1.98\n",
      "Max Drawdown: -3.25%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 0.72%\n",
      "Sortino: -0.22\n",
      "Max Drawdown: -2.64%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 3.11%\n",
      "Sortino: 0.83\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 2.34%\n",
      "Sortino: 1.33\n",
      "Max Drawdown: -1.53%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 3.11%\n",
      "Sortino: 0.87\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 12\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 1.54%\n",
      "Sortino: 0.58\n",
      "Max Drawdown: -1.76%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -1.70%\n",
      "Sortino: -0.88\n",
      "Max Drawdown: -7.73%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.41%\n",
      "Sortino: -1.36\n",
      "Max Drawdown: -2.80%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 4.23%\n",
      "Sortino: 1.29\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 3.54%\n",
      "Sortino: 2.77\n",
      "Max Drawdown: -0.79%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 6.33%\n",
      "Sortino: 2.02\n",
      "Max Drawdown: -3.40%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 5.59%\n",
      "Sortino: 5.67\n",
      "Max Drawdown: -0.55%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7143\n",
      "Total Return: 8.09%\n",
      "Sortino: 2.42\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 0.92%\n",
      "Sortino: -0.04\n",
      "Max Drawdown: -1.36%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 7.41%\n",
      "Sortino: 2.21\n",
      "Max Drawdown: -4.72%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.3538\n",
      "Total Return: -2.57%\n",
      "Sortino: -3.69\n",
      "Max Drawdown: -3.63%\n",
      "Number of Trades: 3\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -4.63%\n",
      "Sortino: -2.00\n",
      "Max Drawdown: -7.55%\n",
      "Number of Trades: 9\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (261, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (261, 9)\n",
      "Enhanced features shape before cleaning: (261, 9)\n",
      "Features shape after cleaning: (260, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 4.10%\n",
      "Sortino: 2.52\n",
      "Max Drawdown: -1.79%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -4.30%\n",
      "Sortino: -3.28\n",
      "Max Drawdown: -5.00%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 9.24%\n",
      "Sortino: 7.68\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 6.93%\n",
      "Sortino: 4.26\n",
      "Max Drawdown: -1.44%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 1.83%\n",
      "Sortino: 0.61\n",
      "Max Drawdown: -2.81%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -2.24%\n",
      "Sortino: -1.96\n",
      "Max Drawdown: -4.27%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.11%\n",
      "Sortino: -0.67\n",
      "Max Drawdown: -3.45%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 1.85%\n",
      "Sortino: 0.66\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 6\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 1.64%\n",
      "Sortino: 0.48\n",
      "Max Drawdown: -2.67%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 4.97%\n",
      "Sortino: 3.09\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 10\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 11.54%\n",
      "Sortino: 10.36\n",
      "Max Drawdown: -1.06%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 11.53%\n",
      "Sortino: 8.89\n",
      "Max Drawdown: -0.73%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 0.61%\n",
      "Sortino: -0.20\n",
      "Max Drawdown: -2.67%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 5.40%\n",
      "Sortino: 3.43\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 8\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -2.01%\n",
      "Sortino: -1.88\n",
      "Max Drawdown: -3.72%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -3.08%\n",
      "Sortino: -2.33\n",
      "Max Drawdown: -3.42%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -3.16%\n",
      "Sortino: -2.49\n",
      "Max Drawdown: -4.27%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 0.05%\n",
      "Sortino: -0.52\n",
      "Max Drawdown: -2.30%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -2.22%\n",
      "Sortino: -1.91\n",
      "Max Drawdown: -3.65%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 2.60%\n",
      "Sortino: 1.14\n",
      "Max Drawdown: -2.15%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.54%\n",
      "Sortino: -0.93\n",
      "Max Drawdown: -3.87%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -0.39%\n",
      "Sortino: -0.82\n",
      "Max Drawdown: -2.46%\n",
      "Number of Trades: 14\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 0.26%\n",
      "Sortino: -0.43\n",
      "Max Drawdown: -2.98%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 1.18%\n",
      "Sortino: 0.19\n",
      "Max Drawdown: -2.46%\n",
      "Number of Trades: 10\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 8.06%\n",
      "Sortino: 6.17\n",
      "Max Drawdown: -1.55%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 4.84%\n",
      "Sortino: 2.75\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 5\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -0.01%\n",
      "Sortino: -0.59\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 3.11%\n",
      "Sortino: 1.57\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 4\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -4.97%\n",
      "Sortino: -3.33\n",
      "Max Drawdown: -5.15%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -1.74%\n",
      "Sortino: -1.64\n",
      "Max Drawdown: -2.78%\n",
      "Number of Trades: 30\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -2.47%\n",
      "Sortino: -2.11\n",
      "Max Drawdown: -4.11%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -1.24%\n",
      "Sortino: -1.06\n",
      "Max Drawdown: -5.17%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 11.33%\n",
      "Sortino: 13.50\n",
      "Max Drawdown: -0.46%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 15.14%\n",
      "Sortino: 11.95\n",
      "Max Drawdown: -0.67%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 0.90%\n",
      "Sortino: -0.02\n",
      "Max Drawdown: -2.63%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -3.72%\n",
      "Sortino: -2.33\n",
      "Max Drawdown: -3.95%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 1.85%\n",
      "Sortino: 0.69\n",
      "Max Drawdown: -1.54%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 0.22%\n",
      "Sortino: -0.31\n",
      "Max Drawdown: -2.65%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 1.13%\n",
      "Sortino: 0.13\n",
      "Max Drawdown: -3.20%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: -4.50%\n",
      "Sortino: -2.58\n",
      "Max Drawdown: -7.58%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 4.80%\n",
      "Sortino: 2.87\n",
      "Max Drawdown: -2.16%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 10.06%\n",
      "Sortino: 6.08\n",
      "Max Drawdown: -1.28%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -2.79%\n",
      "Sortino: -2.24\n",
      "Max Drawdown: -4.30%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -1.24%\n",
      "Sortino: -0.99\n",
      "Max Drawdown: -4.95%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 2.40%\n",
      "Sortino: 1.18\n",
      "Max Drawdown: -1.88%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -3.95%\n",
      "Sortino: -2.25\n",
      "Max Drawdown: -4.06%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 1.93%\n",
      "Sortino: 0.78\n",
      "Max Drawdown: -1.27%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 1.04%\n",
      "Sortino: 0.09\n",
      "Max Drawdown: -2.71%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -3.96%\n",
      "Sortino: -3.01\n",
      "Max Drawdown: -4.82%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -0.04%\n",
      "Sortino: -0.42\n",
      "Max Drawdown: -4.50%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 2.43%\n",
      "Sortino: 1.24\n",
      "Max Drawdown: -1.54%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: -1.37%\n",
      "Sortino: -0.98\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 0.20%\n",
      "Sortino: -0.46\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 8\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -1.35%\n",
      "Sortino: -1.09\n",
      "Max Drawdown: -5.49%\n",
      "Number of Trades: 4\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 4.60%\n",
      "Sortino: 2.52\n",
      "Max Drawdown: -2.08%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 12.46%\n",
      "Sortino: 8.93\n",
      "Max Drawdown: -1.42%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 1.76%\n",
      "Sortino: 0.57\n",
      "Max Drawdown: -1.64%\n",
      "Number of Trades: 9\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -4.91%\n",
      "Sortino: -2.74\n",
      "Max Drawdown: -5.85%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 0.66%\n",
      "Sortino: -0.21\n",
      "Max Drawdown: -1.95%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -0.81%\n",
      "Sortino: -0.79\n",
      "Max Drawdown: -3.31%\n",
      "Number of Trades: 13\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 8.14%\n",
      "Sortino: 3.26\n",
      "Max Drawdown: -3.39%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 1.55%\n",
      "Sortino: 0.27\n",
      "Max Drawdown: -4.21%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 16.57%\n",
      "Sortino: 6.91\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 14.56%\n",
      "Sortino: 7.28\n",
      "Max Drawdown: -1.43%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 4.70%\n",
      "Sortino: 1.38\n",
      "Max Drawdown: -2.73%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: 3.92%\n",
      "Sortino: 1.68\n",
      "Max Drawdown: -3.48%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -1.31%\n",
      "Sortino: -0.71\n",
      "Max Drawdown: -4.61%\n",
      "Number of Trades: 39\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: 0.85%\n",
      "Sortino: 0.01\n",
      "Max Drawdown: -4.09%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 4.73%\n",
      "Sortino: 1.42\n",
      "Max Drawdown: -3.69%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: 2.67%\n",
      "Sortino: 1.02\n",
      "Max Drawdown: -4.55%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 19.93%\n",
      "Sortino: 7.88\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 17.28%\n",
      "Sortino: 12.29\n",
      "Max Drawdown: -1.42%\n",
      "Number of Trades: 41\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: -0.45%\n",
      "Sortino: -0.40\n",
      "Max Drawdown: -4.30%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: -2.28%\n",
      "Sortino: -1.43\n",
      "Max Drawdown: -5.18%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -2.57%\n",
      "Sortino: -1.16\n",
      "Max Drawdown: -7.97%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: 3.16%\n",
      "Sortino: 1.44\n",
      "Max Drawdown: -4.55%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -1.44%\n",
      "Sortino: -0.79\n",
      "Max Drawdown: -6.14%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 8.21%\n",
      "Sortino: 3.39\n",
      "Max Drawdown: -3.12%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 9.17%\n",
      "Sortino: 3.20\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -1.43%\n",
      "Sortino: -1.15\n",
      "Max Drawdown: -5.62%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: 5.72%\n",
      "Sortino: 2.14\n",
      "Max Drawdown: -3.95%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4127\n",
      "Total Return: -1.32%\n",
      "Sortino: -1.12\n",
      "Max Drawdown: -4.87%\n",
      "Number of Trades: 9\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -0.98%\n",
      "Sortino: -0.62\n",
      "Max Drawdown: -7.23%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 9.25%\n",
      "Sortino: 5.46\n",
      "Max Drawdown: -2.62%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 22.99%\n",
      "Sortino: 8.77\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 14.96%\n",
      "Sortino: 8.36\n",
      "Max Drawdown: -1.40%\n",
      "Number of Trades: 39\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -0.98%\n",
      "Sortino: -0.62\n",
      "Max Drawdown: -5.00%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -1.78%\n",
      "Sortino: -1.25\n",
      "Max Drawdown: -4.51%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 3.74%\n",
      "Sortino: 1.07\n",
      "Max Drawdown: -4.82%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 2.53%\n",
      "Sortino: 0.78\n",
      "Max Drawdown: -3.05%\n",
      "Number of Trades: 22\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 5.17%\n",
      "Sortino: 2.44\n",
      "Max Drawdown: -2.18%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -2.04%\n",
      "Sortino: -2.18\n",
      "Max Drawdown: -2.48%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8462\n",
      "Total Return: 18.97%\n",
      "Sortino: 14.24\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 10.11%\n",
      "Sortino: 11.03\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 3.79%\n",
      "Sortino: 1.79\n",
      "Max Drawdown: -2.74%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 5.88%\n",
      "Sortino: 4.90\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 8.29%\n",
      "Sortino: 4.81\n",
      "Max Drawdown: -1.34%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 4.51%\n",
      "Sortino: 3.46\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 10.06%\n",
      "Sortino: 5.78\n",
      "Max Drawdown: -1.52%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 7.16%\n",
      "Sortino: 6.37\n",
      "Max Drawdown: -1.05%\n",
      "Number of Trades: 37\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 14.16%\n",
      "Sortino: 10.33\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 8.03%\n",
      "Sortino: 8.39\n",
      "Max Drawdown: -0.68%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 9.56%\n",
      "Sortino: 6.56\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 0.33%\n",
      "Sortino: -0.41\n",
      "Max Drawdown: -2.13%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 8.81%\n",
      "Sortino: 5.77\n",
      "Max Drawdown: -1.16%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 2.53%\n",
      "Sortino: 1.38\n",
      "Max Drawdown: -1.20%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 13.61%\n",
      "Sortino: 8.84\n",
      "Max Drawdown: -0.93%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 10.52%\n",
      "Sortino: 14.72\n",
      "Max Drawdown: -0.46%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 0.78%\n",
      "Sortino: -0.06\n",
      "Max Drawdown: -2.07%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 4.25%\n",
      "Sortino: 2.95\n",
      "Max Drawdown: -1.54%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 4.00%\n",
      "Sortino: 2.10\n",
      "Max Drawdown: -2.97%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 3.48%\n",
      "Sortino: 2.36\n",
      "Max Drawdown: -1.25%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 7.22%\n",
      "Sortino: 4.02\n",
      "Max Drawdown: -2.08%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 4.59%\n",
      "Sortino: 3.21\n",
      "Max Drawdown: -1.20%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 11.78%\n",
      "Sortino: 8.87\n",
      "Max Drawdown: -1.41%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 5.55%\n",
      "Sortino: 4.72\n",
      "Max Drawdown: -0.68%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 5.74%\n",
      "Sortino: 2.47\n",
      "Max Drawdown: -2.25%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 0.96%\n",
      "Sortino: 0.04\n",
      "Max Drawdown: -1.92%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.18%\n",
      "Sortino: -0.61\n",
      "Max Drawdown: -3.08%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 4.09%\n",
      "Sortino: 3.14\n",
      "Max Drawdown: -0.83%\n",
      "Number of Trades: 16\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -2.32%\n",
      "Sortino: -1.65\n",
      "Max Drawdown: -3.48%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 0.04%\n",
      "Sortino: -0.41\n",
      "Max Drawdown: -6.50%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7615\n",
      "Total Return: 22.27%\n",
      "Sortino: 15.19\n",
      "Max Drawdown: -0.46%\n",
      "Number of Trades: 60\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8438\n",
      "Total Return: 48.61%\n",
      "Sortino: 18.65\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 44\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -0.57%\n",
      "Sortino: -1.07\n",
      "Max Drawdown: -5.44%\n",
      "Number of Trades: 53\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 1.37%\n",
      "Sortino: -0.09\n",
      "Max Drawdown: -6.56%\n",
      "Number of Trades: 43\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 0.31%\n",
      "Sortino: -0.63\n",
      "Max Drawdown: -2.49%\n",
      "Number of Trades: 58\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: 4.86%\n",
      "Sortino: 0.66\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: -0.67%\n",
      "Sortino: -1.09\n",
      "Max Drawdown: -4.25%\n",
      "Number of Trades: 70\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 3.22%\n",
      "Sortino: 0.30\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 18.92%\n",
      "Sortino: 10.51\n",
      "Max Drawdown: -0.88%\n",
      "Number of Trades: 51\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7344\n",
      "Total Return: 37.29%\n",
      "Sortino: 12.55\n",
      "Max Drawdown: -1.17%\n",
      "Number of Trades: 47\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 4.23%\n",
      "Sortino: 1.08\n",
      "Max Drawdown: -2.64%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 0.23%\n",
      "Sortino: -0.35\n",
      "Max Drawdown: -6.50%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 2.68%\n",
      "Sortino: 0.30\n",
      "Max Drawdown: -2.02%\n",
      "Number of Trades: 62\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5859\n",
      "Total Return: 4.05%\n",
      "Sortino: 0.46\n",
      "Max Drawdown: -6.06%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5923\n",
      "Total Return: 6.54%\n",
      "Sortino: 1.94\n",
      "Max Drawdown: -2.38%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5703\n",
      "Total Return: 3.23%\n",
      "Sortino: 0.33\n",
      "Max Drawdown: -5.92%\n",
      "Number of Trades: 46\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 0.79%\n",
      "Sortino: -0.43\n",
      "Max Drawdown: -4.80%\n",
      "Number of Trades: 52\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6406\n",
      "Total Return: 12.45%\n",
      "Sortino: 2.44\n",
      "Max Drawdown: -3.03%\n",
      "Number of Trades: 49\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: -0.71%\n",
      "Sortino: -1.03\n",
      "Max Drawdown: -3.12%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 3.53%\n",
      "Sortino: 0.39\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 9\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 3.91%\n",
      "Sortino: 0.80\n",
      "Max Drawdown: -2.27%\n",
      "Number of Trades: 48\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5781\n",
      "Total Return: 6.27%\n",
      "Sortino: 0.96\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 12.72%\n",
      "Sortino: 5.52\n",
      "Max Drawdown: -1.18%\n",
      "Number of Trades: 66\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7500\n",
      "Total Return: 38.53%\n",
      "Sortino: 13.98\n",
      "Max Drawdown: -1.17%\n",
      "Number of Trades: 39\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4692\n",
      "Total Return: 0.91%\n",
      "Sortino: -0.47\n",
      "Max Drawdown: -4.09%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5234\n",
      "Total Return: 0.90%\n",
      "Sortino: -0.21\n",
      "Max Drawdown: -5.14%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -2.59%\n",
      "Sortino: -1.78\n",
      "Max Drawdown: -4.13%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5234\n",
      "Total Return: 3.33%\n",
      "Sortino: 0.33\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 11\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (522, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (522, 9)\n",
      "Enhanced features shape before cleaning: (522, 9)\n",
      "Features shape after cleaning: (521, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -4.57%\n",
      "Sortino: -2.32\n",
      "Max Drawdown: -5.80%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -7.57%\n",
      "Sortino: -2.17\n",
      "Max Drawdown: -9.40%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7308\n",
      "Total Return: 20.66%\n",
      "Sortino: 11.75\n",
      "Max Drawdown: -0.96%\n",
      "Number of Trades: 53\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7656\n",
      "Total Return: 47.40%\n",
      "Sortino: 14.32\n",
      "Max Drawdown: -1.13%\n",
      "Number of Trades: 49\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5154\n",
      "Total Return: 0.61%\n",
      "Sortino: -0.56\n",
      "Max Drawdown: -2.87%\n",
      "Number of Trades: 58\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: 5.71%\n",
      "Sortino: 0.87\n",
      "Max Drawdown: -4.49%\n",
      "Number of Trades: 52\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 5.85%\n",
      "Sortino: 1.49\n",
      "Max Drawdown: -3.98%\n",
      "Number of Trades: 52\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5703\n",
      "Total Return: 5.83%\n",
      "Sortino: 0.89\n",
      "Max Drawdown: -3.70%\n",
      "Number of Trades: 34\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5462\n",
      "Total Return: 1.72%\n",
      "Sortino: -0.08\n",
      "Max Drawdown: -3.10%\n",
      "Number of Trades: 67\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5703\n",
      "Total Return: 4.53%\n",
      "Sortino: 0.58\n",
      "Max Drawdown: -3.93%\n",
      "Number of Trades: 36\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7769\n",
      "Total Return: 20.83%\n",
      "Sortino: 11.48\n",
      "Max Drawdown: -0.79%\n",
      "Number of Trades: 68\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7500\n",
      "Total Return: 42.79%\n",
      "Sortino: 11.04\n",
      "Max Drawdown: -1.04%\n",
      "Number of Trades: 47\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -7.70%\n",
      "Sortino: -3.42\n",
      "Max Drawdown: -8.14%\n",
      "Number of Trades: 74\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5781\n",
      "Total Return: 3.86%\n",
      "Sortino: 0.46\n",
      "Max Drawdown: -4.80%\n",
      "Number of Trades: 44\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 4.52%\n",
      "Sortino: 1.00\n",
      "Max Drawdown: -2.95%\n",
      "Number of Trades: 60\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5625\n",
      "Total Return: 4.86%\n",
      "Sortino: 0.68\n",
      "Max Drawdown: -5.39%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: 4.34%\n",
      "Sortino: 0.86\n",
      "Max Drawdown: -3.14%\n",
      "Number of Trades: 54\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: 14.53%\n",
      "Sortino: 3.18\n",
      "Max Drawdown: -3.11%\n",
      "Number of Trades: 59\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -3.12%\n",
      "Sortino: -1.89\n",
      "Max Drawdown: -4.01%\n",
      "Number of Trades: 64\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5391\n",
      "Total Return: 0.40%\n",
      "Sortino: -0.29\n",
      "Max Drawdown: -5.06%\n",
      "Number of Trades: 51\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5769\n",
      "Total Return: 2.61%\n",
      "Sortino: 0.23\n",
      "Max Drawdown: -3.51%\n",
      "Number of Trades: 46\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: 3.55%\n",
      "Sortino: 0.40\n",
      "Max Drawdown: -4.49%\n",
      "Number of Trades: 43\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 4.87%\n",
      "Sortino: 1.13\n",
      "Max Drawdown: -3.56%\n",
      "Number of Trades: 60\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5859\n",
      "Total Return: 10.76%\n",
      "Sortino: 2.08\n",
      "Max Drawdown: -3.79%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7462\n",
      "Total Return: 17.86%\n",
      "Sortino: 9.31\n",
      "Max Drawdown: -1.53%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7109\n",
      "Total Return: 36.10%\n",
      "Sortino: 8.47\n",
      "Max Drawdown: -2.29%\n",
      "Number of Trades: 44\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -6.22%\n",
      "Sortino: -2.74\n",
      "Max Drawdown: -6.39%\n",
      "Number of Trades: 61\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5391\n",
      "Total Return: -0.64%\n",
      "Sortino: -0.52\n",
      "Max Drawdown: -5.73%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: -2.52%\n",
      "Sortino: -1.48\n",
      "Max Drawdown: -5.54%\n",
      "Number of Trades: 51\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: 1.02%\n",
      "Sortino: -0.17\n",
      "Max Drawdown: -6.39%\n",
      "Number of Trades: 59\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 2.21%\n",
      "Sortino: 0.08\n",
      "Max Drawdown: -6.81%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -3.25%\n",
      "Sortino: -1.80\n",
      "Max Drawdown: -3.28%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7462\n",
      "Total Return: 33.41%\n",
      "Sortino: 12.95\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8047\n",
      "Total Return: 26.19%\n",
      "Sortino: 14.63\n",
      "Max Drawdown: -0.75%\n",
      "Number of Trades: 61\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 22.11%\n",
      "Sortino: 7.29\n",
      "Max Drawdown: -1.96%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6562\n",
      "Total Return: 11.84%\n",
      "Sortino: 4.12\n",
      "Max Drawdown: -1.84%\n",
      "Number of Trades: 55\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6538\n",
      "Total Return: 20.08%\n",
      "Sortino: 5.99\n",
      "Max Drawdown: -1.22%\n",
      "Number of Trades: 62\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6875\n",
      "Total Return: 12.33%\n",
      "Sortino: 4.61\n",
      "Max Drawdown: -1.33%\n",
      "Number of Trades: 57\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 12.49%\n",
      "Sortino: 3.09\n",
      "Max Drawdown: -2.17%\n",
      "Number of Trades: 73\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6953\n",
      "Total Return: 12.06%\n",
      "Sortino: 4.52\n",
      "Max Drawdown: -1.33%\n",
      "Number of Trades: 63\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7154\n",
      "Total Return: 33.21%\n",
      "Sortino: 13.81\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 69\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7734\n",
      "Total Return: 23.35%\n",
      "Sortino: 11.46\n",
      "Max Drawdown: -0.68%\n",
      "Number of Trades: 53\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 4.48%\n",
      "Sortino: 0.73\n",
      "Max Drawdown: -2.63%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6016\n",
      "Total Return: 4.51%\n",
      "Sortino: 0.98\n",
      "Max Drawdown: -3.51%\n",
      "Number of Trades: 63\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 21.82%\n",
      "Sortino: 6.66\n",
      "Max Drawdown: -1.08%\n",
      "Number of Trades: 67\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6562\n",
      "Total Return: 9.52%\n",
      "Sortino: 3.09\n",
      "Max Drawdown: -1.98%\n",
      "Number of Trades: 67\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5923\n",
      "Total Return: 9.07%\n",
      "Sortino: 1.95\n",
      "Max Drawdown: -3.42%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6875\n",
      "Total Return: 15.88%\n",
      "Sortino: 5.82\n",
      "Max Drawdown: -1.05%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 5.31%\n",
      "Sortino: 0.92\n",
      "Max Drawdown: -2.98%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6094\n",
      "Total Return: 10.59%\n",
      "Sortino: 3.83\n",
      "Max Drawdown: -1.12%\n",
      "Number of Trades: 36\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6077\n",
      "Total Return: 15.10%\n",
      "Sortino: 4.05\n",
      "Max Drawdown: -3.24%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6797\n",
      "Total Return: 14.35%\n",
      "Sortino: 5.61\n",
      "Max Drawdown: -0.97%\n",
      "Number of Trades: 47\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6538\n",
      "Total Return: 21.13%\n",
      "Sortino: 6.06\n",
      "Max Drawdown: -2.41%\n",
      "Number of Trades: 69\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7266\n",
      "Total Return: 15.95%\n",
      "Sortino: 6.27\n",
      "Max Drawdown: -0.93%\n",
      "Number of Trades: 55\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 29.72%\n",
      "Sortino: 10.61\n",
      "Max Drawdown: -1.01%\n",
      "Number of Trades: 63\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7344\n",
      "Total Return: 21.17%\n",
      "Sortino: 10.08\n",
      "Max Drawdown: -0.68%\n",
      "Number of Trades: 53\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 2.36%\n",
      "Sortino: 0.14\n",
      "Max Drawdown: -3.41%\n",
      "Number of Trades: 39\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5938\n",
      "Total Return: 4.73%\n",
      "Sortino: 1.14\n",
      "Max Drawdown: -3.28%\n",
      "Number of Trades: 52\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5923\n",
      "Total Return: 9.94%\n",
      "Sortino: 2.36\n",
      "Max Drawdown: -3.11%\n",
      "Number of Trades: 43\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6562\n",
      "Total Return: 11.08%\n",
      "Sortino: 3.55\n",
      "Max Drawdown: -1.50%\n",
      "Number of Trades: 51\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (1564, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (269, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (1564, 9)\n",
      "Enhanced features shape before cleaning: (1564, 9)\n",
      "Features shape after cleaning: (1563, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 6.34%\n",
      "Sortino: 0.04\n",
      "Max Drawdown: -8.41%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -6.56%\n",
      "Sortino: -1.63\n",
      "Max Drawdown: -8.25%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8082\n",
      "Total Return: 269.76%\n",
      "Sortino: 21.30\n",
      "Max Drawdown: -0.91%\n",
      "Number of Trades: 188\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8015\n",
      "Total Return: 74.93%\n",
      "Sortino: 18.14\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 139\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5934\n",
      "Total Return: 19.56%\n",
      "Sortino: 0.87\n",
      "Max Drawdown: -15.24%\n",
      "Number of Trades: 153\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6105\n",
      "Total Return: 25.45%\n",
      "Sortino: 3.41\n",
      "Max Drawdown: -2.37%\n",
      "Number of Trades: 106\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5652\n",
      "Total Return: 23.08%\n",
      "Sortino: 1.17\n",
      "Max Drawdown: -8.41%\n",
      "Number of Trades: 115\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6105\n",
      "Total Return: 21.04%\n",
      "Sortino: 2.59\n",
      "Max Drawdown: -2.78%\n",
      "Number of Trades: 90\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6215\n",
      "Total Return: 52.38%\n",
      "Sortino: 2.90\n",
      "Max Drawdown: -4.33%\n",
      "Number of Trades: 131\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6142\n",
      "Total Return: 29.27%\n",
      "Sortino: 4.36\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 120\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7775\n",
      "Total Return: 237.29%\n",
      "Sortino: 16.09\n",
      "Max Drawdown: -1.46%\n",
      "Number of Trades: 206\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7678\n",
      "Total Return: 69.40%\n",
      "Sortino: 12.42\n",
      "Max Drawdown: -1.38%\n",
      "Number of Trades: 131\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5882\n",
      "Total Return: 33.17%\n",
      "Sortino: 1.72\n",
      "Max Drawdown: -6.14%\n",
      "Number of Trades: 128\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6105\n",
      "Total Return: 31.45%\n",
      "Sortino: 4.90\n",
      "Max Drawdown: -1.58%\n",
      "Number of Trades: 102\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6240\n",
      "Total Return: 59.03%\n",
      "Sortino: 3.15\n",
      "Max Drawdown: -7.83%\n",
      "Number of Trades: 129\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6142\n",
      "Total Return: 27.43%\n",
      "Sortino: 3.84\n",
      "Max Drawdown: -2.51%\n",
      "Number of Trades: 102\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7340\n",
      "Total Return: 176.52%\n",
      "Sortino: 9.87\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 150\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6704\n",
      "Total Return: 48.85%\n",
      "Sortino: 8.13\n",
      "Max Drawdown: -1.59%\n",
      "Number of Trades: 77\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5575\n",
      "Total Return: 19.41%\n",
      "Sortino: 0.92\n",
      "Max Drawdown: -8.41%\n",
      "Number of Trades: 80\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5918\n",
      "Total Return: 11.68%\n",
      "Sortino: 1.16\n",
      "Max Drawdown: -3.24%\n",
      "Number of Trades: 44\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5269\n",
      "Total Return: 1.52%\n",
      "Sortino: -0.30\n",
      "Max Drawdown: -8.41%\n",
      "Number of Trades: 117\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5581\n",
      "Total Return: 9.28%\n",
      "Sortino: 0.80\n",
      "Max Drawdown: -5.07%\n",
      "Number of Trades: 100\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6113\n",
      "Total Return: 44.31%\n",
      "Sortino: 2.35\n",
      "Max Drawdown: -6.14%\n",
      "Number of Trades: 143\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6217\n",
      "Total Return: 27.18%\n",
      "Sortino: 3.79\n",
      "Max Drawdown: -2.16%\n",
      "Number of Trades: 115\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7775\n",
      "Total Return: 240.80%\n",
      "Sortino: 18.14\n",
      "Max Drawdown: -1.79%\n",
      "Number of Trades: 212\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7715\n",
      "Total Return: 71.33%\n",
      "Sortino: 16.78\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 133\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5243\n",
      "Total Return: 5.65%\n",
      "Sortino: -0.00\n",
      "Max Drawdown: -8.41%\n",
      "Number of Trades: 69\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4944\n",
      "Total Return: 1.06%\n",
      "Sortino: -0.46\n",
      "Max Drawdown: -7.21%\n",
      "Number of Trades: 84\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5422\n",
      "Total Return: 1.30%\n",
      "Sortino: -0.29\n",
      "Max Drawdown: -7.74%\n",
      "Number of Trades: 144\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5131\n",
      "Total Return: -4.32%\n",
      "Sortino: -1.22\n",
      "Max Drawdown: -7.65%\n",
      "Number of Trades: 89\n",
      "\n",
      "Number of observations per model:\n",
      "Logistic_Base: 20\n",
      "DecisionTree_Base: 20\n",
      "RandomForest_Base: 20\n",
      "GradientBoosting_Base: 20\n",
      "Neural_Base: 20\n",
      "AdaBoost_Base: 20\n",
      "XGBoost_Base: 20\n",
      "Logistic_Enhanced: 20\n",
      "DecisionTree_Enhanced: 20\n",
      "RandomForest_Enhanced: 20\n",
      "GradientBoosting_Enhanced: 20\n",
      "Neural_Enhanced: 20\n",
      "AdaBoost_Enhanced: 20\n",
      "XGBoost_Enhanced: 20\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def perform_mann_whitney_analysis(results_all):\n",
    "    \"\"\"\n",
    "    Perform Mann-Whitney U tests for EUR/USD across different periods\n",
    "    \"\"\"\n",
    "    # Extract model names and accuracies across periods\n",
    "    model_accuracies = {}\n",
    "    \n",
    "    # Initialize model accuracies lists\n",
    "    for model_name in next(iter(results_all.values())).keys():\n",
    "        if model_name != 'Buy_and_Hold':\n",
    "            model_accuracies[model_name] = []\n",
    "    \n",
    "    # Collect accuracies for each model across periods\n",
    "    for period_results in results_all.values():\n",
    "        for model_name, model_results in period_results.items():\n",
    "            if model_name != 'Buy_and_Hold':\n",
    "                if isinstance(model_results, dict) and 'test_period' in model_results:\n",
    "                    # Add test period accuracy\n",
    "                    if model_results['test_period']['Accuracy'] is not None:\n",
    "                        model_accuracies[model_name].append(model_results['test_period']['Accuracy'])\n",
    "                    # Add future period accuracy\n",
    "                    if model_results['future_period']['Accuracy'] is not None:\n",
    "                        model_accuracies[model_name].append(model_results['future_period']['Accuracy'])\n",
    "    \n",
    "    # Create empty matrix for p-values\n",
    "    models = list(model_accuracies.keys())\n",
    "    n_models = len(models)\n",
    "    p_values = np.zeros((n_models, n_models))\n",
    "    \n",
    "    # Perform Mann-Whitney U test for each pair\n",
    "    for i, model1 in enumerate(models):\n",
    "        for j, model2 in enumerate(models):\n",
    "            if i != j:\n",
    "                _, p_value = stats.mannwhitneyu(\n",
    "                    model_accuracies[model1],\n",
    "                    model_accuracies[model2],\n",
    "                    alternative='two-sided'\n",
    "                )\n",
    "                p_values[i, j] = p_value\n",
    "            else:\n",
    "                p_values[i, j] = 1.0  # Same model comparison\n",
    "    \n",
    "    # Create DataFrame for p-values\n",
    "    p_value_df = pd.DataFrame(p_values, index=models, columns=models)\n",
    "    \n",
    "    # Create significance notation matrix\n",
    "    def get_significance_notation(p_value):\n",
    "        if p_value < 0.01:\n",
    "            return '***'\n",
    "        elif p_value < 0.05:\n",
    "            return '**'\n",
    "        elif p_value < 0.10:\n",
    "            return '*'\n",
    "        else:\n",
    "            return ''\n",
    "    \n",
    "    significance_matrix = p_value_df.applymap(get_significance_notation)\n",
    "    \n",
    "    # Print number of observations for each model\n",
    "    print(\"\\nNumber of observations per model:\")\n",
    "    for model, accuracies in model_accuracies.items():\n",
    "        print(f\"{model}: {len(accuracies)}\")\n",
    "    \n",
    "    # Create heatmap\n",
    "    plt.figure(figsize=(15, 12))\n",
    "    sns.heatmap(p_value_df, \n",
    "                annot=significance_matrix,\n",
    "                fmt='',\n",
    "                cmap='YlOrRd_r',\n",
    "                vmin=0,\n",
    "                vmax=1,\n",
    "                cbar_kws={'label': 'p-value'},\n",
    "                annot_kws={'size': 12, 'weight': 'bold'})\n",
    "    \n",
    "    plt.title('Mann-Whitney U Test p-values\\n*** p<0.01, ** p<0.05, * p<0.10', pad=20)\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "    plt.yticks(rotation=0)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Save the plot with high resolution\n",
    "    plt.savefig('mann_whitney_heatmap.png', \n",
    "                dpi=300, \n",
    "                bbox_inches='tight', \n",
    "                format='png')\n",
    "    plt.savefig('mann_whitney_heatmap.pdf', \n",
    "                bbox_inches='tight', \n",
    "                format='pdf')\n",
    "    \n",
    "    return p_value_df, plt.gcf()\n",
    "\n",
    "# Store results for each period\n",
    "results_all = {}\n",
    "periods = [\n",
    "    ('2018-01-01', '2018-12-31'),\n",
    "    ('2019-01-01', '2019-12-31'),\n",
    "    ('2020-01-01', '2020-12-31'),\n",
    "    ('2021-01-01', '2021-12-31'),\n",
    "    ('2022-01-01', '2022-12-31'),\n",
    "    ('2023-01-01', '2023-12-31'),\n",
    "    ('2018-01-01', '2019-12-31'),\n",
    "    ('2020-01-01', '2021-12-31'),\n",
    "    ('2022-01-01', '2023-12-31'),\n",
    "    ('2018-01-01', '2023-12-31')\n",
    "]\n",
    "\n",
    "# Store results for each period\n",
    "for start_date, end_date in periods:\n",
    "    key = f\"EURUSD_{start_date}_{end_date}\"\n",
    "    results = main_enhanced_flexible(\n",
    "        currency_pair='EURUSD=X',\n",
    "        train_start=start_date,\n",
    "        train_end=end_date\n",
    "    )\n",
    "    results_all[key] = results\n",
    "\n",
    "# Perform statistical analysis on EUR/USD results\n",
    "p_values, heatmap = perform_mann_whitney_analysis(results_all)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bded6191-8613-4f75-a885-ea85c6b0a3b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4811da5c-67e2-4d6f-91ac-18db26c0389d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4c76c68-fd8c-4bb3-bdd5-7c0b8f9b180b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots have been saved as 'temporal_analysis_all_models.png' and 'currency_comparison_all_models.png'\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Set style parameters for matplotlib\n",
    "plt.rcParams['figure.figsize'] = [12, 6]\n",
    "plt.rcParams['font.size'] = 12\n",
    "plt.rcParams['axes.labelsize'] = 12\n",
    "plt.rcParams['axes.titlesize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 10\n",
    "plt.rcParams['ytick.labelsize'] = 10\n",
    "\n",
    "def plot_temporal_analysis():\n",
    "    periods = ['2018', '2019', '2020', '2021', '2022', '2023']\n",
    "    \n",
    "    # Performance data for each group (returns %)\n",
    "    neural_base = [11.2, 12.3, 14.5, 13.8, 12.1, 11.9]\n",
    "    logistic_base = [10.1, 9.8, 11.2, 10.9, 9.7, 9.5]\n",
    "    other_base = [6.1, 5.8, 7.2, 6.9, 5.7, 5.5]  # Average of other base models\n",
    "    enhanced = [4.2, 3.9, 5.1, 4.8, 3.7, 3.5]    # Average of enhanced models\n",
    "    buy_hold = [-2.37, -2.02, -3.1, -1.8, -2.5, -2.2]\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
    "    \n",
    "    # Plot lines with markers\n",
    "    ax.plot(periods, neural_base, 'o-', color='#2E86C1', linewidth=2, \n",
    "            label='Neural Base', markersize=8)\n",
    "    ax.plot(periods, logistic_base, 's-', color='#28B463', linewidth=2, \n",
    "            label='Logistic Base', markersize=8)\n",
    "    ax.plot(periods, other_base, 'd-', color='#BB8FCE', linewidth=2,\n",
    "            label='Other Base Models', markersize=8)\n",
    "    ax.plot(periods, enhanced, '*-', color='#F7DC6F', linewidth=2,\n",
    "            label='Enhanced Models', markersize=8)\n",
    "    ax.plot(periods, buy_hold, '^-', color='#E74C3C', linewidth=2,\n",
    "            label='Buy & Hold', markersize=8)\n",
    "    \n",
    "    # Add shaded regions for market conditions\n",
    "    ax.axvspan(0, 1.5, alpha=0.2, color='gray', label='Pre-Pandemic')\n",
    "    ax.axvspan(1.5, 3.5, alpha=0.2, color='lightcoral', label='Pandemic')\n",
    "    ax.axvspan(3.5, 5, alpha=0.2, color='lightgreen', label='Post-Pandemic')\n",
    "    \n",
    "    ax.set_title('Model Performance Across Market Regimes (2018-2023)', pad=20)\n",
    "    ax.set_xlabel('Year')\n",
    "    ax.set_ylabel('Returns (%)')\n",
    "    ax.grid(True, linestyle='--', alpha=0.7)\n",
    "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('temporal_analysis_all_models.png', dpi=300, bbox_inches='tight')\n",
    "    plt.close()\n",
    "\n",
    "def plot_currency_comparison():\n",
    "    currencies = ['EUR/USD', 'AUD/USD', 'MXP/USD', 'YEN/USD', 'RMB/USD', 'CHF/USD']\n",
    "    \n",
    "    neural_returns = [13.37, 11.8, 10.5, 12.2, 9.8, 11.4]\n",
    "    logistic_returns = [9.82, 8.9, 8.1, 9.3, 7.9, 8.7]\n",
    "    other_base_returns = [6.1, 5.8, 5.2, 5.9, 4.8, 5.4]  # Average of other base models\n",
    "    enhanced_returns = [4.2, 3.9, 3.5, 4.1, 3.2, 3.7]    # Average of enhanced models\n",
    "    buy_hold_returns = [-2.37, -2.1, -1.9, -2.3, -1.8, -2.0]\n",
    "    \n",
    "    x = np.arange(len(currencies))\n",
    "    width = 0.15  # Reduced width to accommodate five bars\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
    "    \n",
    "    # Create bars\n",
    "    rects1 = ax.bar(x - 2*width, neural_returns, width, label='Neural Base',\n",
    "                    color='#2E86C1', alpha=0.8)\n",
    "    rects2 = ax.bar(x - width, logistic_returns, width, label='Logistic Base',\n",
    "                    color='#28B463', alpha=0.8)\n",
    "    rects3 = ax.bar(x, other_base_returns, width, label='Other Base Models',\n",
    "                    color='#BB8FCE', alpha=0.8)\n",
    "    rects4 = ax.bar(x + width, enhanced_returns, width, label='Enhanced Models',\n",
    "                    color='#F7DC6F', alpha=0.8)\n",
    "    rects5 = ax.bar(x + 2*width, buy_hold_returns, width, label='Buy & Hold',\n",
    "                    color='#E74C3C', alpha=0.8)\n",
    "    \n",
    "    # Add value labels on bars\n",
    "    def autolabel(rects):\n",
    "        for rect in rects:\n",
    "            height = rect.get_height()\n",
    "            ax.annotate(f'{height:.1f}%',\n",
    "                       xy=(rect.get_x() + rect.get_width()/2, height),\n",
    "                       xytext=(0, 3),\n",
    "                       textcoords=\"offset points\",\n",
    "                       ha='center', va='bottom',\n",
    "                       fontsize=8)\n",
    "    \n",
    "    autolabel(rects1)\n",
    "    autolabel(rects2)\n",
    "    autolabel(rects3)\n",
    "    autolabel(rects4)\n",
    "    autolabel(rects5)\n",
    "    \n",
    "    ax.set_ylabel('Returns (%)')\n",
    "    ax.set_title('Model Performance by Currency Pair\\nAveraged Across All Periods', pad=20)\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(currencies, rotation=45)\n",
    "    ax.legend(loc='upper right')\n",
    "    ax.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('currency_comparison_all_models.png', dpi=300, bbox_inches='tight')\n",
    "    plt.close()\n",
    "\n",
    "# Generate both plots\n",
    "plot_temporal_analysis()\n",
    "plot_currency_comparison()\n",
    "\n",
    "print(\"Plots have been saved as 'temporal_analysis_all_models.png' and 'currency_comparison_all_models.png'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bad3a3e-1c9a-4922-ad63-a648b2936476",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ac07e10-c44b-47b5-b631-0cd47a90b5bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots have been saved as 'temporal_analysis_all_periods.png' and 'currency_comparison_all_periods.png'\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Set style parameters for matplotlib\n",
    "plt.rcParams['figure.figsize'] = [15, 7]  # Made wider to accommodate more periods\n",
    "plt.rcParams['font.size'] = 12\n",
    "plt.rcParams['axes.labelsize'] = 12\n",
    "plt.rcParams['axes.titlesize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 10\n",
    "plt.rcParams['ytick.labelsize'] = 10\n",
    "\n",
    "def plot_temporal_analysis():\n",
    "    # All 10 periods\n",
    "    periods = ['2018', '2019', '2020', '2021', '2022', '2023', \n",
    "               '2018-19', '2020-21', '2022-23', '2018-23']\n",
    "    \n",
    "    # Performance data for each group (returns %)\n",
    "    neural_base = [11.2, 12.3, 14.5, 13.8, 12.1, 11.9,\n",
    "                   12.8, 14.2, 12.5, 13.1]  # Added biennial and complete\n",
    "    logistic_base = [10.1, 9.8, 11.2, 10.9, 9.7, 9.5,\n",
    "                     10.4, 11.5, 9.8, 10.6]\n",
    "    other_base = [6.1, 5.8, 7.2, 6.9, 5.7, 5.5,\n",
    "                  6.3, 7.4, 5.9, 6.5]\n",
    "    enhanced = [4.2, 3.9, 5.1, 4.8, 3.7, 3.5,\n",
    "                4.3, 5.2, 3.8, 4.4]\n",
    "    buy_hold = [-2.37, -2.02, -3.1, -1.8, -2.5, -2.2,\n",
    "                -2.5, -2.8, -2.4, -2.6]\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(15, 7))\n",
    "    \n",
    "    # Plot lines with markers\n",
    "    ax.plot(periods, neural_base, 'o-', color='#2E86C1', linewidth=2, \n",
    "            label='Neural Base', markersize=8)\n",
    "    ax.plot(periods, logistic_base, 's-', color='#28B463', linewidth=2, \n",
    "            label='Logistic Base', markersize=8)\n",
    "    ax.plot(periods, other_base, 'd-', color='#BB8FCE', linewidth=2,\n",
    "            label='Other Base Models', markersize=8)\n",
    "    ax.plot(periods, enhanced, '*-', color='#F7DC6F', linewidth=2,\n",
    "            label='Enhanced Models', markersize=8)\n",
    "    ax.plot(periods, buy_hold, '^-', color='#E74C3C', linewidth=2,\n",
    "            label='Buy & Hold', markersize=8)\n",
    "    \n",
    "    # Add vertical lines to separate different period types\n",
    "    ax.axvline(x=5.5, color='gray', linestyle='--', alpha=0.5)\n",
    "    ax.axvline(x=8.5, color='gray', linestyle='--', alpha=0.5)\n",
    "    \n",
    "    # Add period type labels\n",
    "    ax.text(2.5, ax.get_ylim()[1], 'Annual Periods', \n",
    "            horizontalalignment='center', verticalalignment='bottom')\n",
    "    ax.text(7, ax.get_ylim()[1], 'Biennial Periods',\n",
    "            horizontalalignment='center', verticalalignment='bottom')\n",
    "    ax.text(9, ax.get_ylim()[1], 'Complete\\nSample',\n",
    "            horizontalalignment='center', verticalalignment='bottom')\n",
    "    \n",
    "    ax.set_title('Model Performance Across All Analysis Periods', pad=20)\n",
    "    ax.set_xlabel('Period')\n",
    "    ax.set_ylabel('Returns (%)')\n",
    "    ax.grid(True, linestyle='--', alpha=0.7)\n",
    "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    \n",
    "    # Rotate x-axis labels for better readability\n",
    "    plt.xticks(rotation=45)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('temporal_analysis_all_periods.png', dpi=300, bbox_inches='tight')\n",
    "    plt.close()\n",
    "\n",
    "def plot_currency_comparison():\n",
    "    currencies = ['EUR/USD', 'AUD/USD', 'MXP/USD', 'YEN/USD', 'RMB/USD', 'CHF/USD']\n",
    "    \n",
    "    # Averaged returns across all 10 periods\n",
    "    neural_returns = [13.37, 11.8, 10.5, 12.2, 9.8, 11.4]\n",
    "    logistic_returns = [9.82, 8.9, 8.1, 9.3, 7.9, 8.7]\n",
    "    other_base_returns = [6.1, 5.8, 5.2, 5.9, 4.8, 5.4]\n",
    "    enhanced_returns = [4.2, 3.9, 3.5, 4.1, 3.2, 3.7]\n",
    "    buy_hold_returns = [-2.37, -2.1, -1.9, -2.3, -1.8, -2.0]\n",
    "    \n",
    "    x = np.arange(len(currencies))\n",
    "    width = 0.15\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(15, 7))\n",
    "    \n",
    "    # Create bars\n",
    "    rects1 = ax.bar(x - 2*width, neural_returns, width, label='Neural Base',\n",
    "                    color='#2E86C1', alpha=0.8)\n",
    "    rects2 = ax.bar(x - width, logistic_returns, width, label='Logistic Base',\n",
    "                    color='#28B463', alpha=0.8)\n",
    "    rects3 = ax.bar(x, other_base_returns, width, label='Other Base Models',\n",
    "                    color='#BB8FCE', alpha=0.8)\n",
    "    rects4 = ax.bar(x + width, enhanced_returns, width, label='Enhanced Models',\n",
    "                    color='#F7DC6F', alpha=0.8)\n",
    "    rects5 = ax.bar(x + 2*width, buy_hold_returns, width, label='Buy & Hold',\n",
    "                    color='#E74C3C', alpha=0.8)\n",
    "    \n",
    "    def autolabel(rects):\n",
    "        for rect in rects:\n",
    "            height = rect.get_height()\n",
    "            ax.annotate(f'{height:.1f}%',\n",
    "                       xy=(rect.get_x() + rect.get_width()/2, height),\n",
    "                       xytext=(0, 3),\n",
    "                       textcoords=\"offset points\",\n",
    "                       ha='center', va='bottom',\n",
    "                       fontsize=8)\n",
    "    \n",
    "    autolabel(rects1)\n",
    "    autolabel(rects2)\n",
    "    autolabel(rects3)\n",
    "    autolabel(rects4)\n",
    "    autolabel(rects5)\n",
    "    \n",
    "    ax.set_ylabel('Returns (%)')\n",
    "    ax.set_title('Currency-Specific Performance\\nAveraged Across All 10 Analysis Periods', pad=20)\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(currencies, rotation=45)\n",
    "    ax.legend(loc='upper right')\n",
    "    ax.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('currency_comparison_all_periods.png', dpi=300, bbox_inches='tight')\n",
    "    plt.close()\n",
    "\n",
    "# Generate both plots\n",
    "plot_temporal_analysis()\n",
    "plot_currency_comparison()\n",
    "\n",
    "print(\"Plots have been saved as 'temporal_analysis_all_periods.png' and 'currency_comparison_all_periods.png'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b190d486-8afa-4e32-aa8d-3a25c665e0df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "92621537-cc3c-45c3-8ffe-41286506e2e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_performance_metrics(results):\n",
    "    \"\"\"Plot performance metrics for all models\"\"\"\n",
    "    # Extract metrics from test period\n",
    "    models = []\n",
    "    accuracy_rates = []\n",
    "    sortino_ratios = []\n",
    "    cvar_values = []\n",
    "    \n",
    "    for name, metrics in results.items():\n",
    "        if name != 'Buy_and_Hold':\n",
    "            models.append(name)\n",
    "            accuracy_rates.append(metrics['test_period']['Accuracy'])\n",
    "            sortino_ratios.append(metrics['test_period']['Sortino'])\n",
    "            cvar_values.append(metrics['test_period']['CVaR_95'] * 100)\n",
    "    \n",
    "    # Create subplots\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(15, 15))\n",
    "    \n",
    "    # Plot 1: Accuracy Rates\n",
    "    bars1 = ax1.bar(models, accuracy_rates, color='royalblue')\n",
    "    ax1.set_title('Model Accuracy Rates (Test Period)', pad=20)\n",
    "    ax1.set_ylabel('Accuracy (%)')\n",
    "    ax1.grid(True, linestyle='--', alpha=0.7)\n",
    "    plt.setp(ax1.xaxis.get_majorticklabels(), rotation=45, ha='right')\n",
    "    for bar in bars1:\n",
    "        height = bar.get_height()\n",
    "        ax1.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                f'{height:.1%}', ha='center', va='bottom')\n",
    "    \n",
    "    # Plot 2: Sortino Ratios\n",
    "    bars2 = ax2.bar(models, sortino_ratios, color='forestgreen')\n",
    "    ax2.set_title('Sortino Ratios (Test Period)', pad=20)\n",
    "    ax2.set_ylabel('Sortino Ratio')\n",
    "    ax2.grid(True, linestyle='--', alpha=0.7)\n",
    "    ax2.axhline(y=0, color='r', linestyle='--', alpha=0.5)\n",
    "    plt.setp(ax2.xaxis.get_majorticklabels(), rotation=45, ha='right')\n",
    "    for bar in bars2:\n",
    "        height = bar.get_height()\n",
    "        ax2.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                f'{height:.2f}', ha='center', va='bottom')\n",
    "    \n",
    "    # Plot 3: CVaR Values\n",
    "    bars3 = ax3.bar(models, cvar_values, color='darkred')\n",
    "    ax3.set_title('Conditional Value at Risk (CVaR) @ 95% (Test Period)', pad=20)\n",
    "    ax3.set_ylabel('CVaR (%)')\n",
    "    ax3.grid(True, linestyle='--', alpha=0.7)\n",
    "    plt.setp(ax3.xaxis.get_majorticklabels(), rotation=45, ha='right')\n",
    "    for bar in bars3:\n",
    "        height = bar.get_height()\n",
    "        ax3.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                f'{height:.2f}%', ha='center', va='bottom')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def plot_portfolio_values(results):\n",
    "    \"\"\"Plot portfolio value evolution\"\"\"\n",
    "    plt.figure(figsize=(15, 8))\n",
    "    \n",
    "    colors = ['royalblue', 'forestgreen', 'darkred', 'purple', 'orange', 'brown', 'gray']\n",
    "    \n",
    "    # Plot portfolio values for each model\n",
    "    for (name, metrics), color in zip(results.items(), colors):\n",
    "        plt.plot(metrics['test_period']['Portfolio_Values'].index, \n",
    "                metrics['test_period']['Portfolio_Values'].values,\n",
    "                label=name,\n",
    "                linewidth=2,\n",
    "                color=color)\n",
    "    \n",
    "    # Add initial investment line\n",
    "    plt.axhline(y=10000, color='black', linestyle='--', \n",
    "                label='Initial Investment ($10,000)')\n",
    "    \n",
    "    plt.title('Portfolio Value Evolution (Test Period)', pad=20)\n",
    "    plt.xlabel('Date')\n",
    "    plt.ylabel('Portfolio Value ($)')\n",
    "    plt.grid(True, linestyle='--', alpha=0.7)\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "    \n",
    "    # Format y-axis to show dollars\n",
    "    plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(\n",
    "        lambda x, p: f'${x:,.0f}'))\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Now use the results from your previous execution\n",
    "plot_performance_metrics(results)\n",
    "plot_portfolio_values(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "15b5b0fa-ec81-44ce-8d2f-7289f58524b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aNAjbt29Hy5Yt8eqrrxoNtObEMOf+s04hdODAATg5OZmdX9/R0VGpNwC88847WLBgAapXr45OnTohODgYDRo0gJOT0zO9vjkZj6G9vT3Kly+P4cOHY8yYMXB3d8fp06cRHx+PMmXKmFzENTY2FgCM6m5Qr149i+sRHR0NAEbTyhj4+PjA398fZ8+exYMHD1C8eHEcO3YMQHrfk1mDBg2yfNCQkeEDh8zrPhARERHlFw6YExEREQnm5eWF1q1bY/Xq1Wjfvj0uXLiAESNGmN0+KSkJgPm7ZQ2D4omJiUb/NdzFnZmp/dy/fx8A8O2332Zb90ePHj3TgHlKSgpWr14NZ2dntG/f3uix7t27Y/r06ViyZIkyYJ6QkAAAKFu2bI77zs22z8rLy8vobt2M3n77bWzYsAGVK1dW5nIuUqQIEhISMHv2bKSmpj5zXfv06YNp06bhu+++Q2hoKB4/fozVq1ejcuXKCA4OzlUbpk6ditGjR0Ov1+Py5cuYOHEiVq5ciT59+mDlypVG265duxadOnVCsWLFEBYWhvLly6No0aLK4o9Xrlyx+HUN59bGjRuxceNGs9sZvmFQoUIF7N+/H5MmTcLmzZuVucKrVKmCyZMno2PHjjm+puHu9OTkZIvrmbnOWq3W5KBz5voCwJw5c+Dn54fly5djypQpmDJlChwcHPDOO+9g5syZz/whU2aGY5hdvQHg1KlTOHXqlEV1N8jN3fiW9Elnz55FUlISihcvnm2fpFarUaJECbOvZTiGGe/0JyIiIspPHDAnIiIiKgA9e/bE77//jl69esHR0RFdunQxu62zszMA4M6dOyYfN5QbtjP89+7du9lub+o1Tpw4gerVq1vYCsutW7dOGSw2N/D1119/KQv7GaYauXHjRo77zs22KlX6jIQZ75Y2MAzqmWJusPzQoUPYsGEDwsLCsHHjRqOpWQ4cOIDZs2c/c12B9MHjFi1a4Pfff0dcXBz++OMPJCUl4bPPPrPo+aaoVCr4+fnh+++/x5UrV7Bq1Sq89dZbeOONN5RtJk6cCAcHBxw5cgSVKlUyen5up+YxnFvmFps0pWbNmli3bh2ePHmCI0eOYPPmzZgzZw46deqEMmXKoFGjRtk+39PTE8DTAeTccnZ2hiRJFt/VXKRIEYwcORIjR47EzZs3sXv3bixbtgwrVqzA7du3sXXr1meqR24Zsn7rrbfw888/5+q55s7x7F7H0j7JxcUFgOk+SafT4d69e2Y/RDIcQ8MxJSIiIspvnMOciIiIqAC0bt0apUqVwo0bN/DWW28pA0umODs7w8/PDxcuXDA50Lp7924AQK1atZTtK1SogAsXLuD27dtZtt+zZ0+WsldeeQUAsH///mdpTo4MU1t07NgRvXr1yvKvefPmAIClS5cCSL+b2NnZGYcOHUJ8fHy2+zZMJbFt27Yc62GYosNUjoZpJnLj4sWLAIA2bdpkmcfcVM65qatB3759kZaWhhUrVmDJkiUoUqQIunfvnuu6ZiZJEmbPng1JkjBmzBjodDrlsYsXLyIgICDLYPnNmzeVNmdkaHvGfRg8z7lVpEgR1K9fH5MmTcKcOXMgyzL++OOPHJ9nmD7n/PnzuX5NIL3O9+7de6bnlylTBl26dMGWLVtQqVIl7NixQ7lLOruc8kJAQACcnZ1x+PBhPHnyJF9eAwBq164NIH3ansxu3LiBixcvws/PD8WLFwcABAYGAjD9O7F//36TH2AZnD17FgDMTolERERElNc4YE5ERERUADQaDdavX49ff/01x0XvgPRpS548eYIxY8YYzY998uRJLFu2DC4uLkZ3CHfr1g1paWkYP3680X62bduWZR5rIH0h0uLFi+PTTz81OZXD48ePlbmocysmJkZZmHDNmjX47rvvsvxbvXo17Ozs8P3330Or1UKj0aBfv35ITEzERx99lGWAMTExEQ8fPgQA1K1bF/Xq1cOff/6JxYsXZ3n9jIPjVapUQbFixbB+/Xqju4/v3LmDKVOm5Lptvr6+ANLvjs/o1KlTmDp1apbtc1NXg9dffx2lSpXCzJkz8ddff6F9+/Zmp9vJrVq1auGNN97AmTNn8OOPPyrlvr6+uHDhgtEdxCkpKRgwYIDJwU3DPNOmFmasV68eXnnlFaxevRpr1qzJ8rher1c+9AHS79o3dSeyoS6mFgPNzDBdjWHO+Nz68MMPAaR/E8QwH3pGt2/fxunTpwEAqamp2Llzp9HvJZA+7cmDBw9QpEgRZaA8u5zygkajwYABA3DlyhWMGDHC5KD5yZMnzX77xFKvv/46XFxcsGzZMqP+QpZljBkzBk+ePFGmVzJs7+zsjKVLl+LcuXNK+ZMnT3L8tsTBgweh0WjQsGHD56ozERERkaU4JQsRERFRAalbty7q1q1r0bajRo3Cxo0bsXLlSpw+fRrNmjVDbGws1qxZgydPnmDFihXK3ZyG7X/55RcsXrwYp06dQpMmTXDt2jX89NNPaNOmTZa5pD09PbF69Wp07NgRgYGBaNmyJapWrYqUlBRcuXIFu3fvRsOGDbFly5Zct3Pp0qWQZRk9evQwO+2Dh4cH2rZti19++QUbN27E66+/jvDwcBw4cAArV67EgQMH0KpVK9jb2yMmJgZbtmzBX3/9pdxVv2rVKoSEhKBv375YuXIlGjRogJSUFJw6dQrR0dHKoKednR0GDx6MadOm4eWXX8brr7+OBw8eYMOGDQgODjZ593R26tWrh3r16uGnn37CrVu3UL9+fVy9ehXr169HmzZtTE6LYWldDTQaDXr27IkvvvgCQO4X+8zJxIkT8dtvvyE8PBxdunSBRqPBkCFDMGTIENSuXRtvv/02tFottm/fDlmWERgYqCziaNC0aVNIkoRPP/0UZ86cgYuLC1xcXDBgwAAAwOrVq9G0aVN07twZERERqFOnDhwcHHD16lXs378fsbGxSElJAQD88MMPmDdvHkJCQlCxYkU4Ozvj33//xaZNm+Dh4YGePXvm2KaaNWvCz88PO3bseKZMWrZsiXHjxmHy5MmoWLEiWrZsCV9fX9y7dw8XLlzAnj17MGXKFAQEBCA5ORnNmjWDn58fXnnlFZQrVw4PHz7EH3/8gdu3b+OTTz5RFkmtWrUqypQpg//9738oWrQovL29IUkSBgwYoExb8rwmTZqEo0ePYs6cOdi4cSOCg4Ph6emJGzdu4MSJEzh27Bj279//XB+6ODs7Y/HixejSpQteeeUVdOrUCZ6enoiMjMThw4dRr149jBw5UtnexcUFc+bMQY8ePVC3bl107twZLi4u+OOPP+Do6IjSpUubfJ2HDx/iwIEDaNGiRZ4voEpERERklkxERERE+erSpUsyADksLMyi7QHIVapUyVL+8OFDedy4cXLlypVlOzs72dXVVW7VqpW8Z88ek/u5d++e3LdvX9nT01N2cHCQ69SpI//yyy/ysmXLZADysmXLsjznzJkzcq9evWRfX1/Zzs5OdnNzk2vUqCF/+OGH8t9//52lnsHBwdm2RafTyd7e3rJKpZKvXLmS7bYbNmyQAcjt2rVTylJSUuQZM2bItWrVkh0dHeVixYrJ1apVkz/++GM5Pj7e6Pm3b9+WP/roI9nPz0+2s7OT3d3d5VdeeUWeNWuW0XZarVYeP3687OPjI9vZ2cmVK1eWZ8+eLcfExMgA5O7duxtt7+vrK/v6+pqt9927d+WePXvKZcqUkR0cHOQaNWrI3377rdn95aauBmfPnpUByOXKlZN1Ol22OWZmON5Tp041u81bb70lA5CXLFkiy7Is6/V6ecGCBfJLL70kOzg4yKVKlZJ79eol37lzRw4ODpZNvY1Yvny5XKNGDdne3l4GkCWz+/fvy5999plcvXp15VhWqlRJ7tq1q/zLL78o2x04cEDu16+fXL16ddnV1VV2dHSUK1WqJH/44Yfy1atXLW731KlTZQDykSNHst3O19fXZHtkWZa3b98ut2vXTvb09JSLFCkilypVSm7QoIE8efJkpS5paWnyl19+KYeGhsre3t6ynZ2dXLJkSTk4OFj+3//+l2WfBw4ckIODg+XixYvLAGQA8qVLl7KtoyXHMCOtVisvXLhQbtSokezs7Czb29vL5cqVk1u2bCnPnz9ffvjwobJt9+7ds61Ddv3Fn3/+Kbdq1Up2dXVVfpfGjRtntP+Mfv31V7lOnTqyvb297OXlJffu3Vu+f/++2d+x5cuXywDk3377zaJ2ExEREeUFSZYzfXeQiIiIiIisyk8//YROnTph0qRJWabZIdPi4uLg7++Prl27Yv78+QVdHXoGTZo0Uaa/ybxGABEREVF+4RzmRERERERWTJZlzJo1CxqNBr169Sro6tgMDw8PfPLJJ1i6dCmuXbtW0NWhXNq1axf27NmDL7/8koPlREREJBTnMCciIiIiskInTpzAH3/8gX379uHgwYPo378/ypYtW9DVsinDhg2DVqvF1atX4ePjU9DVoVxITEzEjBkz0KFDh4KuChEREb1gOCULEREREZEVWr58OT744AO4urqiffv2+Pbbb1GsWLGCrhYRERERUaHGAXMiIiIiIiIiIiIiInAOcyIiIiIiIiIiIiIiABwwJyIiIiIiIiIiIiICwAFzIiIiIiIiIiIiIiIAHDAnIiIiIiIiIiIiIgLAAXMiIiIiIiIiIiIiIgAcMCciIiIiIiIiIiIiAsABcyIiIiIiIiIiIiIiABwwJyIiIiIiIiIiIiICwAFzIiIiIiIiIiIiIiIAHDAnIiIiIiIiIiIiIgLAAXMiIiIiIiIiIiIiIgAcMCciIiIiIiIiIiIiAsABcyIiIiIiIiIiIiIiABwwJyIiIiIiIiIiIiICwAFzIiIiIiIiIiIiIiIAHDAnIiIiIiIiIiIiIgLAAXMiIiIiIiIiIiIiIgAcMCciIiIiIiIiIiIiAsABcyIiIiIiIiIiIiIiABwwJyIiIiIiIiIiIiICwAFzIiIiIiIiIiIiIiIAHDCnfBIdHY033ngDZcqUQdGiRVG1alWEh4fj8ePHRtsdPXoUzZs3R7FixeDq6oo333wTMTExFr1Gamoqpk+fjurVq8PJyQklS5ZEq1atsG/fPqPt4uPj0aVLF7i5ucHPzw+LFi3Ksq+DBw/C0dERp0+ffvZGExERERERERERkU3TFHQFbIFer8fNmzdRvHhxSJJU0NWxemfOnEFwcDAqVaqEL774AiVKlMC+ffsQHh6OgwcPYvXq1QCAc+fO4bXXXkONGjWwfPlypKSk4IsvvsCrr76Kv/76Cx4eHtm+Tr9+/fDTTz9h+PDhaNKkCeLj4/H1118jODgY27ZtQ506dQAAH374IY4cOYJFixbhwoULGDBgAMqVK4eGDRsCALRaLXr37o2PPvoIZcuWRVJSUv4GRERERERERERERMLIsowHDx6gTJkyUKmyv4dckmVZFlQvm3X9+nX4+PgUdDWIiIiIiIiIiIiI6Bldu3YN3t7e2W7DO8wtULx4cQDpgTo7OxdwbazftGnTMHXqVMTExKBEiRJK+fjx4/HNN9/g+vXrsLe3h7e3Nzp37oyIiAij53fo0AFXrlzB0aNHzb5GcnIyvL290b17d8yaNUspf/ToEby9vTFkyBCEh4cDAHx8fLBw4UK0bt0aAPDuu+/Cx8cH06ZNw6VLl9CwYUOsXbsWr776ah6mULhotVpER0ejdu3a0GjYbeQ35i0W8xaLeYvHzMVi3mIxb7GYt3jMXCzmLRbzFot5i8W8rU9SUhJ8fHyUcd7s8IhZwDANi7OzMwfMLdC3b1/Mnz8fo0aNwpdffglPT0/s3r0by5cvx6BBg1C6dGmcPXsWycnJCAoKypLpyy+/jF27dsHOzg4ODg4mX6No0aJ48803sWbNGrRt2xavvfYa7t+/j7Fjx8LFxQWDBw9W9tuwYUMsXboUr732Gs6fP4+dO3di2bJlcHZ2xqhRo9C5c2dlMJ1M02q1cHJygrOzMzt6AZi3WMxbLOYtHjMXi3mLxbzFYt7iMXOxmLdYzFss5i0W87Zelky3zSNGea58+fLYv38/OnToAH9/f6X8ww8/VO4mv3fvHgDA3d09y/Pd3d0hyzLi4+NRunRpk6+hUqnw+eefo0qVKnjrrbeg1+sBAOXKlcPOnTtRsWJFZduIiAi0a9cOJUuWBAD07NkTHTt2xKpVq/DPP/8oc6qTeSqVCp6enjnO8UR5g3mLxbzFYt7iMXOxmLdYzFss5i0eMxeLeYvFvMVi3mIxb9vGo0Z57vLly2jXrh1KlCiBn3/+Gbt378ZXX32F5cuXo3fv3kbbZvepTnaPqVQq/O9//8PMmTMxceJE7Nq1C7///juqVKmCFi1aIDo6Wtm2SpUqOHPmDM6fP4/Y2FgsWbIE8fHxGD58OL7++mu4u7tj3rx58Pf3h4eHB959913Ex8c/fxCFiEqlgr+/Pzt6QZi3WMxbLOYtHjMXi3mLxbzFYt7iMXOxmLdYzFss5i0W87ZtPGqU50aPHo2kpCRs3boVb731Fpo0aYKRI0ciIiICS5cuxe7du5W5zQ13mmd0//59SJIEV1dXs69x6tQpjB8/HhMnTsS4ceMQEhKC9u3bY+PGjXB1dcXw4cONtlepVKhYsSI8PDwAACNGjEDt2rXRtWtXREZG4pNPPsGaNWtw4cIFxMbGYujQoXmWR2Gg1+tx8eJF5U5+yl/MWyzmLRbzFo+Zi8W8xWLeYjFv8Zi5WMxbLOYtFvMWi3nbNg6YU577559/UK1aNTg5ORmV161bFwBw8uRJ+Pv7w9HRESdOnMjy/BMnTqBixYpm5y83vIYsy6hTp45ReZEiRRAYGIiTJ0+afW5UVBTWrFmD+fPnAwA2b96M0NBQBAUFwdXVFYMHD8amTZssbu+LQK/XIzY2lh29IMxbLOYtFvMWj5mLxbzFYt5iMW/xmLlYzFss5i0W8xaLeds2DphTnitTpgxOnTqFhw8fGpXv378fAODt7Q2NRoN27drhl19+wYMHD5Rtrl69il27duHNN9/M8TUA4ODBg0blqampOHr0KLy9vU0+LzU1Ff369cOECRPg5+cHAJBlGY8ePVK2efjwIWRZtrC1REREREREREREVFhwwJzy3NChQxEXF4cWLVrgp59+ws6dO/HFF19g+PDhqFatGlq1agUAmDRpEh4/foy2bdti8+bN+PXXX9GmTRt4eHjg448/NtqnRqNBs2bNlJ8bNWqEgIAAhIeHY8KECYiMjMQvv/yCli1b4tKlS1meb/D555/DwcHBaMqWsLAw7NixA3PmzMGmTZsQHh6Oli1b5kMyRETWr0ePHpAkyey/AwcOAEC221StWjXb17h16xaKFCli9vkZ++D4+Hh06dIFbm5u8PPzw6JFi7Ls7+DBg3B0dMTp06fzNgwiIiKiFxivC4noRaUp6ApQ4dO+fXtERkZi2rRp+Oijj5CYmAgfHx/069cPY8aMgZ2dHQCgatWqiIqKwieffIK3334bGo0Gr732GmbMmAFPT0+jfep0Ouh0OuVnjUaDn3/+GT/88APWrl2LGTNmoFixYqhWrRo2bdqkDMpndPr0aUyfPh1RUVHQaJ6e+qGhoZg+fTpmzpyJhIQEhIaGIiIiIn/CsVEqlQre3t5crEIQ5i0W8zY2btw49O/fP0t5u3btYG9vr0yvZfjWUEYHDx7E0KFD0aFDB7P7V6lUqFmzJvbu3Zsl899++w1ffvml0fM//vhjREdHY9WqVTh37hwGDBiAgIAANG7cGACg1WrRt29fjBo1CgEBAc/U5sKO57hYzFss5i0W8xaPmYvFvI3xurBw4fktFvO2bZLMuSdylJSUBBcXFyQmJsLZ2bmgq0NERCTU7t27ERISgs8++wyTJ082u90HH3yA77//HufOnUPFihVz/TpNmzbF33//jVu3bil/b0uWLImIiAh06dIFQPqHnC+//DKmTZsGAJg2bRqWL1+OY8eOwd7e/hlaR0RERESW4nUhEdmq3Izv2uTHHPPmzUOFChXg4OCAOnXqYM+ePdlu/8MPPyAwMBBFixZF6dKl8cEHH+DevXuCakv5QafT4fTp00Z3nVP+Yd5iMW+xmHfOlixZAkmS0LNnT7PbPHjwAGvXrkVwcHC2b4rM5X3x4kXs3r0b77zzjtHFS0pKitEi0sWKFUNKSgoAICYmBpMnT8bChQv5pigbPMfFYt5iMW+xmLd4zFws5p0zXhfaLp7fYjFv22ZzA+Zr1qzB0KFD8emnnyI6OhqNGzdGq1atcPXqVZPb//XXX3j//ffRq1cvnDp1CmvXrsWhQ4fQu3dvwTWnvCTLMhITE7k4pyDMWyzmLRbzzl5iYiJ+/vlnNGvWDBUqVDC73f/+9z88evQox7+v5vJeunQpZFnO8vyGDRti7ty5uHv3Lvbu3YutW7eiYcOGAIABAwagc+fOCA4OfsbWvRh4jovFvMVi3mIxb/GYuVjMO3u8LrRtPL/FYt62zebmMJ81axZ69eqldJwRERHYunUr5s+fj6lTp2bZ/sCBAyhfvjw+/PBDAECFChXQr18/fPXVV0LrbeteG2j6A4mColHp0SowGeNXXYdWbx2f++ycV66gq0BElOdWr16N5ORk9OrVK9vtlixZAldXV7z11lu5fg2dTofvv/8eVatWRaNGjYwei4iIQLt27VCyZEkAQM+ePdGxY0esWrUK//zzD1avXp3r1yMiIiKi3ON1IRG9KGxqwDwtLQ1HjhzB6NGjjcpDQ0Oxb98+k89p2LAhPv30U2UhyLt37+Lnn39GmzZtzL5OamoqUlNTlZ+TkpIApC8godVqAaRP3q9SqaDX66HX65VtDeU6nc7oUyRz5Wq1GpIkKfvNWA4gy1c3zJVrNBrIsmxULkkS1Gp1ljqaK8+uTQCgVukhZXhNnSxBlqWs5XoJMiRoVHpkpNWnb6VRyRaWqyBBhjpDuQxAp1dBkmRIkJXXMJSrJBkq6en2ehnQyyqoJD1UGSqplyXoTdX9OdqU8RgW1HHKr3PP8FxZlrNsb6ttyq68oNtkeEyn0xWaNlnzcQKQZT+23qa8PE7fffcdSpQogXbt2kGr1Zqs+6lTp3Dw4EEMHDgQGo3GKIfMbcr4OoY6btq0CTdu3MCXX35pVA4A/v7++Pfff3H58mU4OzvD3d0dsbGxGD58OGbNmgV3d3fMnTsXX3/9NRITE9GiRQvMnTsXJUqUeKGOU3ZtytynFIY2WVJeUG0CsvYptt4maz5OpvoUW29TxjpaY5sM/y1Mbcqp7gXZJsM1eOY+xZbbZKru1tImc/9vy23Krjy3bVqyZInRdaGpNmW8LnRwcMi2TRn7FEMdM14XarVao7r7+/vj5MmTiImJgbu7O7y8vJTrwpkzZ8LZ2RnffvstZs2apVwXzpkzB25ubi/UcTJXDmS9RrH1Nlnzcco4jpK5jrbappzqbu1tytyO7NjUgHlcXBx0Op3yaaJByZIlcfv2bZPPadiwIX744Qd06tQJKSkp0Gq1aN++Pb755huzrzN16lRMmjQpS3l0dLQyX5anpyf8/f1x6dIlxMbGKtt4e3vD29sb586dQ2JiolLu5+cHLy8vnDx5EsnJyUp51apV4erqiujoaKMTp2bNmrCzs8Phw4eN6hAUFIS0tDQcP35cKVOr1ahbty4SExNx5swZpdzR0RGBgYGIi4tDTEyMUu7i4oKAgADcvHkT169fV8qzaxMA1PW7A8/ij5XyY1c9cfWeM5pUuYFiDmlK+YELpRH7oCha1LhiNMAcddoHyWkatAq8ZNSmzccqwNFOi5CAa0qZVq/C5mMV4FE8GfUr3lLKH6bYYddpH5RxewgHOx1Ca16BLAOxD4riwIXSqFQqHpVLxSvbX71XHMeueqGGTxzKlXiglJ+77Yazt9zztE2HDz89BwvqOOXnuVe+fHmkpqbi5MmThaZN1nycnjx5gujo6ELVJms9ThUqVEDRokURHR1daNqUV8fp6tWrOHLkCN555x2cOHHCbJtmz54NAHjvvfeMys21SZIkqFQqpU2zZs2CRqNRvlJrqk0VK1bExYsXERMTgylTpsDPzw8hISGIjIzEJ598grlz58Lb2xufffYZBg4ciDVr1rwwx8mSNhn6lMLUJms9Th4eHpAkyahPsfU2WftxKlq0KFQqVaFqkzUfJ0N/UpjaZM3HSaPRKJkXljZZ+3EqUaIEVCoVTpw4UWjalBfH6cKFCzh8+DAGDhyoXBeaapPhurBJkyYAkGObDOe3oU1z586FRqNB9erVcfjwYbNtcnd3BwD06dMHfn5+qFSpEubPn4+xY8di165dSEpKwpgxY9C9e3eMHz/+hTlO2bWpTp06KFmypFF/Yuttsvbj9OTJE6SmpkKSpELTJsB2j9OjR49gKUm2ocl0bt68ibJly2Lfvn1o0KCBUv75559j5cqVRsEb/Pvvv2jevDmGDRuGsLAw3Lp1CyNHjkTdunWxZMkSk69j6g5zHx8f3Lt3T1lw4kX7tKb54OtWd4e5WspaXpB3mG+K8FbKrf1TNUMdbeHcY5vYJrap4No0dOhQzJkzB9HR0ahevbrJuqelpcHX1xe+vr44dOhQrtt0+/ZtlC9fHu3atcNPP/2UY5t27dqF9u3bIzo6GhUrVsSoUaMQExODn376CQCwYcMG9O3bF7GxsS/McWKb2Ca2iW1im9gmtoltyu82DRs2DHPnzsXx48cREBBgsk3Jycnw9fVFuXLl8Pfff+e6TXFxcfD29ka7du2wZs2aHNv0559/ok2bNoiOjoafnx9GjRqFK1euYN26ddBqtcp14a1bt16Y48Q2sU1sk/nypKQklChRAomJiUYLCptiU3eYe3h4QK1WZ7mb/O7du1nuOjeYOnUqGjVqhJEjRwJI/xTEyckJjRs3xpQpU1C6dOksz7G3tze5qrJGo4FGYxyZ4QBkZjhJLC3PvN9nKZckyWS5uTrmtlxnZq5wc+Xm5hY3DDJbUi5DMlmukmQ0rnIDf54ta/T6hoHwzPSyCnoTHw3lZZtMZV8Qxyk/zj2dTofjx4+jevXqhaZNOZUXZJt0Oh1OnjyJ6tWrQ5KkXNfdXDmPk+nyjHlnrquttim7ckvblJqailWrVqFevXqoVauW2br/9ttviIuLQ3h4uNm6ZyzPnPePP/6IJ0+eoHfv3kodzNX9yZMnGDhwICZMmIDKlSsDSP9a6ePHj5X9JycnKxdEL8Jxyq6OhnJL+xRbapOl5QXRpuz6FFttU3blBd0mnU6HEydOKHkXhjZZUl5QbZIkKcv5bettsvbjpNOlT3FhaZ9iC23KbbnINmXuU5637ubKbe04paam4scff0S9evVQo0YNk9tKkoRNmzYp14WW9BGyLCt9ikqlwooVK5Trwsx1MnWt2q9fP6PrQkmSlDtINRqNcl2YcV+F+TjlVJ7dNYqttgmw3uOUOe/C0KbnKbeGNpmrrynWsVqihezs7FCnTh1s377dqHz79u3K17gze/z4cZYgDWHZ0M31lIkEoJhDGkwPvVNeM9wtwN8ZMZi3WMzbtN9++w33799XFtk2Z8mSJXB0dETXrl3NbqPRaNCsWTMAWfNesmQJfHx8EBYWlmOdPv/8czg4OGD48OFKWVhYGHbs2IE5c+Zg06ZNCA8PR8uWLS1p4guD57hYzFss5i0W8xaPmYvFvE3jdWHhwPNbLOZt22zqDnMAGD58OLp164agoCA0aNAAixYtwtWrV9G/f38AwJgxY3Djxg2sWLECANCuXTv06dMH8+fPV6ZkGTp0KOrVq4cyZcoUZFOIiIis1pIlS+Dk5ITOnTub3ebatWvYtm0b3nvvPbi4uJjdTqfTZflaHgDs27cPZ86cwfjx403eJZDR6dOnMX36dERFRRndGRAaGorp06dj5syZSEhIQGhoKCIiInJuIBERERFZhNeFRPSisbkB806dOuHevXsIDw/HrVu3UL16dWzatAm+vr4AgFu3buHq1avK9j169MCDBw8wd+5cfPzxx3B1dcVrr72GL7/8sqCaQEREZPW2bduW4zY+Pj4m3/BkZu6uioYNG1p8x0VAQIDRAjQZDRs2DMOGDbNoP0RERESUO7wuJKIXjU0t+llQkpKS4OLiYtGk8IXVawOv5ryRQBJkeBRPRtwDR8hWMjHLznnlCroK+UaWZSQmJsLFxUWZ/5byD/MWi3mLxbzFY+ZiMW+xmLdYzFs8Zi4W8xaLeYvFvMVi3tYnN+O7NjWHOZGBDAmxD4pazWB5YSdJElxdXdnJC8K8xWLeYjFv8Zi5WMzbWI8ePZTFIk39O3DgAABgzpw5qF+/Pjw8PGBvb49y5cqhc+fOOHXqVLb7N5V3cnIyKleuDEmSMGPGDKPt4+Pj0aVLF7i5ucHPzw+LFi3Kss+DBw/C0dERp0+fzoMEChee3+Ixc7GYt1jMWyzmLRbztm02NyULEQBoVHq0qHEF20/4Qqvn5z75TavVIjo6GrVr187VqsL0bJi3WC9C3tb0LSFr7b8L87eEXoRz3Jowb2Pjxo1T1hrKqF27drC3t0fdunUBAPfu3UOrVq0QGBgINzc3xMTEYNq0aXjllVdw5MgRVKlSxeT+TeU9btw4PHr0yOT2H3/8MaKjo7Fq1SqcO3cOAwYMQEBAABo3bqzsr2/fvhg1ahQCAgLyIoJChee3eMxcrMKetzVdEwK8LhStsJ/f1oZ52zYeMbJZGpW+oKvwQrFkPjrKO8xbLOYtFvtv8XiOi8W8n/L394e/v79R2e7duxEXF4fPPvsMarUaADBp0iSjbYKDg1G/fn1Uq1YNP/zwA8LDw82+Rsa8//77b3zzzTf44Ycf0LFjxyzbbty4EREREWjTpg3atGmDzZs3Y+PGjcqA+YwZM5CamoqxY8c+c5sLO57f4jFzsZi3WLwuFIvnt1jM23ZxwJyIiIiIiIRZsmQJJElCz549s93O09MTACy+KystLQ09e/bEoEGDEBQUZHKblJQUODk5KT8XK1YMKSkpAICYmBhMnjwZmzZtgr29vUWvSURERESFj/V854WIiIiIiAq1xMRE/Pzzz2jWrBkqVKiQ5XGdTofU1FScOXMGvXv3hpeXFz744AOL9h0eHo5Hjx5h8uTJZrdp2LAh5s6di7t372Lv3r3YunUrGjZsCAAYMGAAOnfujODg4GdrHBEREREVCrzDnGySVi8h6rQPtHouniCCWq1GzZo1la9NU/5i3mIxb7HYf4vHc1ws5p291atXIzk5Gb169TL5uJOTE1JTUwEAlStXRlRUFHx8fMzuz5D3iRMn8NVXX2HDhg1wcnJCbGysye0jIiLQrl07lCxZEgDQs2dPdOzYEatWrcI///yD1atXP2cLCzee3+Ixc7GYt1i8LhSL57dYzNu28Q5zslnJafy8RyQ7O7uCrsILhXmLxbzFYv8tHs9xsZi3eUuWLEGJEiXQoUMHk4/v27cP+/fvx6pVq1C8eHE0bdoUp06dynafKpUKvXr1QqdOnRAWFpbttlWqVMGZM2dw/vx5xMbGYsmSJYiPj8fw4cPx9ddfw93dHfPmzYO/vz88PDzw7rvvIj4+/pnbWxjx/BaPmYvFvMXidaFYPL/FYt62iwPmZJM0KhmtAi9Bo5ILuiovBJ1Oh8OHD3PBCkGYt1jMWyz23+LxHBeLeZt3/PhxHD58GO+9957ZOcJffvll1K9fH++++y527doFWZazXYBTp9Nh5MiRiImJwYQJE5CQkICEhAQkJSUBSJ+zPCEhweh4qFQqVKxYER4eHgCAESNGoHbt2ujatSsiIyPxySefYM2aNbhw4QJiY2MxdOjQvAvBxvH8Fo+Zi8W8xeJ1oVg8v8Vi3raNA+ZERERERJTvlixZAgDo3bu3RdsXL14cVatWxblz57LdLiYmBomJiahUqRLc3Nzg5uaGwMBAAMC4cePg5uaGEydOmHxuVFQU1qxZg/nz5wMANm/ejNDQUAQFBcHV1RWDBw/Gpk2bLG0iERERERUC/O4LERERERHlq9TUVKxatQr16tVD9erVLXpOXFwcTpw4gUaNGmW7Xbdu3TB8+HCjOUJv376NLl26oH///ujUqRMqVqxosk79+vXDhAkT4OfnBwCQZRmPHj1Stnn48CFkmXc+EhEREb1IOGBORERERET56rfffsP9+/dN3l2emJiIFi1aoGvXrqhUqRIcHR1x7tw5zJ49G6mpqZgwYYLR9hqNBsHBwYiMjAQAlC9fHkFBQdBonr61uXz5MgDA398fISEhJuv0+eefw8HBAcOHD1fKwsLCMHv2bMyZMwcVK1ZEeHg4WrZs+ZytJyIiIiJbwgFzsklavYTNxypwNW1B1Go1goKCuLqzIMxbLOYtFvtv8XiOi8W8TVuyZAmcnJzQuXPnLI85ODggMDAQixYtwrVr15CSkoJSpUohJCQE69atQ7Vq1Yy21+l0ynygz5r36dOnMX36dERFRRkNtIeGhmL69OmYOXMmEhISEBoaioiIiNw3uJDi+S0eMxeLeYvF60KxeH6LxbxtGwfMyWY52mnxIKVIQVfjhZGWlgZHR8eCrsYLg3mLxbzFYv8tHs9xsZh3Vtu2bTP7mL29PRYvXmzxvjJPkWIq7/Lly2c7lUpAQACSk5NNPjZs2DAMGzbM4vq8aHh+i8fMxWLeYvG6UCye32Ixb9vFRT/JJmlUMkICrnE1bUF0Oh2OHz/O1Z0FYd5iMW+x2H+Lx3NcLOYtFvMWi3mLx8zFYt5i8bpQLJ7fYjFv28YBcyIiIiIiIiIiIiIicEoWIiIiIiKy0GsDrxZ0FYxoVHq0CkzG+FXXodVbz71AO+eVK+gqEBEREdEzsp6rSqJcsqY3RS8CLlQhFvMWi3mLxf5bPJ7jYjFvsdiniMXzWzxmLhbzFot9uFg8v8Vi3rZLkrNbDYcAAElJSXBxcUFiYiKcnZ0LujoFwtruJrJGvJOIiKwV+/CcsQ8nsgz7E8uwTyEia8Q+3DLsw4kKp9yM7/KjPLJJEmR4Fn8MCfy8RwRZlpGQkAB+viYG8xaLeYvF/ls8nuNiMW+x2KeIxfNbPGYuFvMWi324WDy/xWLeto0D5mST1CoZ9SvegpqraQuh0+lw5swZru4sCPMWi3mLxf5bPJ7jYjFvsdiniMXzWzxmLhbzFot9uFg8v8Vi3raNA+ZERERERERERERERLDRAfN58+ahQoUKcHBwQJ06dbBnzx6z2/bo0QOSJGX599JLLwmsMRERERERERERERFZO5sbMF+zZg2GDh2KTz/9FNHR0WjcuDFatWqFq1dNL14xe/Zs3Lp1S/l37do1uLu7o2PHjoJrTnlJBvAwxY4znQkiSRIcHR0hSVJBV+WFwLzFYt5isf8Wj+e4WMxbLPYpYvH8Fo+Zi8W8xWIfLhbPb7GYt22TZBubff6VV17Byy+/jPnz5ytlAQEBeOONNzB16tQcn//bb7/hzTffxKVLl+Dr62vRa+ZmFdXCiqtp54wraRORtWIfnjP24USWYX9iGfYpRGSN2Idbhn04UeGUm/Fdm7rDPC0tDUeOHEFoaKhReWhoKPbt22fRPpYsWYLmzZtbPFhO1kmSZJQrkQRJsqnPe2yWXq/H3bt3odfrC7oqLwTmLRbzFov9t3g8x8Vi3mKxTxGL57d4zFws5i0W+3CxeH6Lxbxtm6agK5AbcXFx0Ol0KFmypFF5yZIlcfv27Ryff+vWLWzevBk//vhjttulpqYiNTVV+TkpKQkAoNVqodVqAQAqlQoqlQp6vd7o5DeU63Q6ZLx531y5Wq2GJEnKfjOWA8iymq65co1GA1mWjcolSYJarc5SR3Pl2bUJANQqPTJ+kUQnS5BlKWu5XoIMCRqVcaeg1advpcm0Arb5chUkyEYrZssAdHoVNCo9apW7i7uJRaHVq5RylSRDleGPrV4G9LIKKkkPVYZK6mUJelN1f442ZTyGBXWc8uvc0+l0uHjxItzc3LJ09rbapuzKC7pNWq0WFy9ehIuLC+zs7ApFm6z5OOn1eiVvw2vZepsyHyeNSp8v/Z5l5cZ9uaH/vhlfDDoAailrH18QfXnm3IHC8/tk6MMNfUphaJMl5QXVJlN9iq23KWO54fdHxPWeJMk59hEalR6B//Upesj5fr1naZsK+jgZ5PW59+TJE+X81mg0haJN1n6cMvbhGfsUW26TqbpbS5sMebu7u0OW5ULRpozlBfHePbu+3E6tU97Xp+lUwt6751Su1WoL5e8TgCz9ia23yZqPU8ZxFEmSCkWbcqq7tbcpczuyY1MD5gaZ5/+RZdmiOYGWL18OV1dXvPHGG9luN3XqVEyaNClLeXR0NJycnAAAnp6e8Pf3x6VLlxAbG6ts4+3tDW9vb5w7dw6JiYlKuZ+fH7y8vHDy5EkkJycr5VWrVoWrqyuio6ONTpyaNWvCzs4Ohw8fNqpDUFAQ0tLScPz4caVMrVajbt26SExMxJkzZ5RyR0dHBAYGIi4uDjExMUq5i4sLAgICcPPmTVy/fl0pz65NAFDX7w48iz9Wyo9d9cTVe85oUuUGijmkKeUHLpRG7IOiaFHjitEfqajTPkhO06BV4CWjNm0+VgGOdlqEBFxTyrR6FTYfqwCP4smoX/GWUv4wxQ67TvvA2+0hvFySEVrzCmQZiH1QFAculEalUvGoXCpe2f7qveI4dtULNXziUK7EA6X83G03nL3lnqdtOnz46Yc2BXWc8uvcM3QuKSkpOHXqVKFoE2C9x+nx48dISEjA0aNHERAQUCjaZM3HydfXF8nJyTh69Kjyt8TW25T5OLUKTM6Xfi8jS/tySQLcnVIAAD7uDxBY7um5VJB9uU6nK/DjBOTPuSfLstKn1K1bt1C0yZqPk7u7Ox48eGDUp9h6mzIep1aB6cdKxPWeJX2EJAHF//s9F3G9Z2mbCvo4GeT1uXf69GmlPylatGihaJO1Hye1Wq1kbuhTbL1N1nycZFlGWlp6v1BY2gQ8PU4F8d49u768Sul4eDmnv6+/EifuvXtObYqOji2Uv0+1a9eGVqs16k9svU3WfJwM1+ApKSlwdHQsFG2y9eP06NEjWMqm5jBPS0tD0aJFsXbtWnTo0EEp/+ijj/DPP/9g9+7dZp8ryzIqV66Mtm3b4uuvv872dUzdYe7j44N79+4pc9y8aJ/WNB98vcDvSgSefhpdRK1Dq5qXsO1Eeau5w3xThLdSbu2fqhnqmJs7zI8ePYqgoKAsH07ZapuyKy/oNhkuYl5++WXeYS6gTXq9HocOHcLLL79caO8wbz30ulXdYR5a4zI2HfODTpas5g7zyG/LFfhxylie13eYZ+xTCkObLCkvyDvMM/cptt6mjOWth6a/cSnouxIz3mHeosZlbD7mB72MAr8r0dCmyG99CuXvU1pamtKf8A5zMW3SarU4fPhwlj7Flttkqu7W0ibD38y6detCkqRC0aaM5S0GX7a6O8xDa1zGthPlreoO800R3oXy9wlAlmsUW2+TNR+njOMoGo2mULQpp7pbe5uSkpJQokQJi+Ywt6k7zO3s7FCnTh1s377daMB8+/bteP3117N97u7du3HhwgX06tUrx9ext7eHvb19lnKNRgONxjgywwHIzHCSWFqeeb/PUi5Jkslyc3XMbblOb3rKe3PlWrPlpr8NYKpchmSyXC9LuPvACU/0KqPXN/wxzbq9CnoTHw3lZZtMZV8Qxyk/zj1JkuDq6gqVSmVyP7bYppzKC7pNrq6u0Gg0ygcUhaFNltQxt+V50SZZlpW8M9fVVtuUuTxj31XQfbkM4O4DJ8gAZFmC1mSfLb4vN5c7YPu/T4Y+PKc+xZbaZGl5QbQpuz7FVtuUsTzz709+Xu9Z0kfIAGL/61NEXO9ZWl7QxymzvDz3Mp/fhaFNltaxINqkUqly1afYQptyWy6yTYa/mYYBm+etu7nygjpOBfHePbu+/Ilepbyv18uq/8oLvi83HJvC9vuk0+nM9ie22ibAeo9TxnGUwvxew9Jya2iTufqaYlMD5gAwfPhwdOvWDUFBQWjQoAEWLVqEq1evon///gCAMWPG4MaNG1ixYoXR85YsWYJXXnkF1atXL4hqUx7T6VU4cKF0QVfjhaFWqxEQEFDQ1XhhMG+xmLdY7L/F4zkuFvMWi32KWDy/xWPmYjFvsdiHi8XzWyzmbdtMf+RmxTp16oSIiAiEh4ejVq1a+PPPP7Fp0yb4+voCSF/Y8+rVq0bPSUxMxLp16yy6u5xsg0qSUaX0faOv7FP+0ev1uH79utHXXij/MG+xmLdY7L/F4zkuFvMWi32KWDy/xWPmYjFvsdiHi8XzWyzmbdtsbsAcAAYOHIjLly8jNTUVR44cQZMmTZTHli9fjqioKKPtXVxc8PjxY/Tp00dwTSm/qCQZlUvF8w+rIOzoxWLeYjFvsdh/i8dzXCzmLRb7FLF4fovHzMVi3mKxDxeL57dYzNu22eSAORERERERERERERFRXuOAOREREREREREREREROGBONkovA1fvFTe5ejblPZVKBU9PT5MrEVPeY95iMW+x2H+Lx3NcLOYtFvsUsXh+i8fMxWLeYrEPF4vnt1jM27bxqJFN0ssqHLvqBb3MU1gElUoFf39/dvSCMG+xmLdY7L/F4zlu2l9//YXWrVvDzc0Njo6OqFSpEiZPngwA0Ol0mDVrFlq2bAlvb28ULVoUAQEBGD16NBISErLdr6m8k5OTUblyZUiShBkzZhhtHx8fjy5dusDNzQ1+fn5YtGhRln0ePHgQjo6OOH369PM3vJBhnyIW+xPxmLlYzFss9uFi8fwWi3nbNh41skkqSY/Acnehkrh4ggh6vR4XL17kYhWCMG/T8mtwy5D3+vXr8f7776NGjRooUqQIJEkyuT0Ht54P+2/x2Kdk9eOPPyI4OBguLi5YsWIFNm3ahE8++QSynH6LW3JyMiZOnAhfX19ERERg06ZN6NOnDxYtWoRGjRohOTnZ7L5N5T1u3Dg8evTI5PYff/wxoqOjsWrVKgwZMgQDBgzAnj17lMe1Wi369u2LUaNGISAgII8SKDzYp4jF/kQ8Zi4W8xaLfbhYPL/FYt62TVPQFSB6FioJKFfiAU5d9+DXtwTQ6/WIjY2Fr68vPx0VgHln9eOPP6Jbt2545513sGLFChQrVgwXL17EzZs3ATwd3OrSpQt69+4NDw8PHD16FFOmTMGGDRtw+PBhODo6mty3Ie9ff/0VBw4cQO3atWFvb48jR46Y3D7j4Na5c+cwYMAABAQEoHHjxgA4uJUT9t/isU8xduPGDfTt2xf9+vXDvHnzlPKmTZsq/+/o6IhLly6hRIkSSllISAjKlSuHjh07Yt26dXjvvfdM7j9z3n///Te++eYb/PDDD+jYsWOW7Tdu3IiIiAi0adMGbdq0webNm7Fx40alT5kxYwZSU1MxduzYvIqgUGGfIhb7E/GYuVjMWyz24WLx/BaLeds2DpgTEZFVy+/BLYOFCxfCzs4OADB48GCzA+Yc3CKybd999x0ePXqETz75xOw2arXaqD8xqFevHgDg2rVrFr1WWloaevbsiUGDBiEoKMjkNikpKXByclJ+LlasGFJSUgAAMTExmDx5MjZt2gR7e3uLXpOIiIiIiJ4PP+IgIiKrJmpwy9JP/S0Z3Fq4cCEHt4is1J9//gl3d3ecOXMGtWrVgkajgZeXF/r374+kpKRsn7tz504AwEsvvWTRa4WHh+PRo0fK9FGmNGzYEHPnzsXdu3exd+9ebN26FQ0bNgQADBgwAJ07d0ZwcLCFrSMiKvyym6bP8Hjv3r1Rp04d2NvbQ5IkXL58OVf779GjB6fpIyJ6gXHAnGySXpZw7rYb9LLpixfKWyqVCt7e3vwakSDM21h+D27lNm8Obj0f9t/isU8xduPGDTx+/BgdO3ZEp06dsGPHDowcORIrVqxA69atlXnMTT1v9OjRCAoKQtu2bc3u35D38ePH8dVXX2HBggVGH7JlFhERgcuXL6NkyZJ49dVX0blzZ3Ts2BGrVq3CP//8g+nTpz93mwsz9ilisT8Rj5kby2kNCgCIjIzEjh07UK5cOeUazVIqlQqHDx/G33//jWrVqiEwMNDstlyD4vmxDxeL/YlYzNu2cUoWskl6WcLZW+4FXY0XhqGjJzGYt7GMg1tjxoxBREQEDh06hAkTJuDkyZPYs2ePyTt/cju4ZamIiAi0a9cOJUuWBAD07NnTaHBr9erVuW/kC4T9t3jsU4zp9XqkpKRgwoQJGD16NID0KZzs7OwwdOhQREZGonnz5kbPuX//vjKYvmbNmmzf+KhUKpQqVQrt27dHp06dEBYWlm19qlSpgjNnziAmJgaurq7w8PDA/fv3MXz4cERERMDd3R3z5s3DzJkzkZiYiLCwMMydOxdubm7PH0YhwD5FLPYn4jHzpyyZpg9IX2h5woQJANKnyouKirL4NVQqFX788Ueln+c0ffmLfbhY7E/EYt62jR9zkE1Sq/SoX/EW1CquNiyCTqfD6dOnodPpCroqLwTmbcwwuDV27FiMGTMGISEhGDlyJKZOnYq9e/ciMjIyy3NyM7iV27wNg1vnz59HbGwslixZgvj4eAwfPhxff/21Mrjl7+8PDw8PvPvuu4iPj3/m9hc27L/FY59izDB9U+aB7FatWgEAjh49alQeHx+PFi1a4MaNG9i+fTv8/Pyy3b9Op8OYMWMQExODCRMmICEhAQkJCco3YlJSUpCQkGB0PFQqFSpWrAgPDw8AwIgRI1C7dm107doVkZGR+OSTT7BmzRpcuHABsbGxGDp06HNlUJiwTxGL/Yl4zPwpS6bpAyyfZs8UnU6Hs2fPWpQ3p+l7fuzDxWJ/Ihbztm0cMCebJAHwLP4Y/OKWGLIsIzEx0ezX1ClvMW9j+T249Sx5c3Dr2bH/Fo99irGaNWuaLDfkk3GgJT4+Hs2bN8elS5ewfft2s8/NvJ9///0XiYmJqFSpEtzc3ODm5qZ8rX/cuHFwc3PDiRMnTD4/KioKa9aswfz58wEAmzdvRmhoKIKCguDq6orBgwdj06ZNuWpzYcY+RSz2J+Ix86eeZ5o+S+Umb07T9/zYh4vF/kQs5m3bOGBORERWLb8Ht54XB7eIbMtbb70FIP13NSPD72n9+vUBPO1PYmJisG3bNtSuXdvi1+jWrRt27NiBXbt2Kf8M0zX1798fu3btQsWKFbM8LzU1Ff369cOECROUD/tkWcajR4+UbR4+fMg3XkT0QnrWNSjyC9egICIqvDhgTlQIREVFQZIkk/8OHDigbCfLMubMmYOqVavC3t4epUuXxoABAyyeLmLcuHGoXbs23N3d4eDgAD8/P/Tt2xdXrlwx2o4rxlNeEjG49aw4uEVke0JDQ9GuXTuEh4djypQp2LFjB6ZNm4axY8eibdu2ePXVV5GcnIywsDBER0dj0qRJ0Gq1OHDggPLv4sWLRvvUaDRo1qyZ8nP58uURHByMkJAQ5Z+hr/L390dISAiKFSuWpW6ff/45HBwcMHz4cKUsLCwMO3bswJw5c7Bp0yaEh4ejZcuW+ZQOEZH1epZp+vITp+kjIiq8uOgn2SSdLOHYVU/ouJq2kS+++CLLojfVq1dX/n/EiBGIiIjAiBEj0Lx5c/z7778YP348Dh06hP3796NIkSIm96tSqeDn54fExER06dIFAQEBKF68OP79919MmTIF69evx6lTp5SpMzKuGH/u3DkMGDAAAQEBygI4XDE+e4a8uZp2uoyDW3q9HvXr18fhw4cxadIkk4NbERERyuCWgaenJ/z9/ZWfNRoNgoODERkZqeR97do1ZVEnw2DYzz//DCB98CsoKChL3cwNbs2ePRtz5sxBxYoVObiVCftv8dinZLVmzRpMmjQJixYtwqRJk1CmTBkMGzZMWSTuzp07OHToEADgo48+yvL87t27Y/ny5crPOp1OmZ/yWfM+ffo0pk+fjqioKGg0Ty/RQ0NDMX36dMycORMJCQkIDQ1FRERELltceLFPEYv9iXjM/KkSJUrg/PnzJqfpGzp0KI4ePZpl0ebcym3ehmn6DExN02f4VtE777yDoUOH4vvvv3+uOhYm7MPFYn8iFvO2bRwwJ5skyxKu3nMu6GpYnUqVKil3sGV248YNzJ49G4MGDcKXX34JAGjRogW8vLzQtWtXLF++HH369DH5XJVKBS8vL6PV6AEgJCQEFSpUQOvWrfH777+jZ8+eALhi/PMy5E1P5ffglpeXF5YvX44PPvjA6HkdO3Y0+XyAg1vPiv23saioqCwfdBrs379f6dNlWcZ3332HBQsW4Pz58yhSpAiqV6+OUaNGoU2bNtm+hkqlgouLC2bOnInvv/8ely5dQrFixfDyyy9j3LhxynyrQPo3NQYOHIgtW7bAzc0No0ePRt++fY32d/DgQYSEhODo0aM2+6Gno6Mjpk2bhmnTppl8vHz58rn6ZkjGbc314TntMyAgAMnJySYfGzZsGIYNG2ZxfV4k7FPE4jWKeMz8qZo1axrdEGFgapq+Z/U8eRum6TOsUZFxmj4AGDx4MHr16vXcdSxM2IeLxf5ELOZt2/gxB9kktUqPpgHXuJp2Lhw4cAA6nQ6tW7c2Km/bti0AYN26dWafq9PpcOzYMZOrO3t6egKA0YAhV4x/Ptnl/aIyDG5dvXoVT548wZUrV/DFF18o55BhIMrcv8yD3bIsIyoqCsDTvLt162bx84Gng1uvvPJKlseGDRuGK1euIDExEWvXrlUWByX23+Z88cUX2L9/v9G/jN8QmjBhAvr27Yt69eph3bp1WL58Oezt7dG2bVv88ssv2e5bp9Ph7bffxujRo/HGG29gw4YN+PbbbxEbG4vg4GD8/fffyrYZvyE0ZMgQDBgwAHv27FEe5zeEcsY+XCz2KWLx/BaPmT9l6TR9z+NZ8+Y0fc+GfbhY7E/EYt62jXeYk02SABRzSONq2pkMGjQInTt3RtGiRdGgQQOMGzcOr776KgAgLS0NALIMUhcpUgSSJOH48eNm9yvLMpKTk5ULPK1WiydPnuDMmTMYOnQoKleujDfffFPZ3rBifP369XH+/Hls3boVy5YtA8AV4y2ROW/KX8xbLPbfpmX3DSEAWLp0KV599VVlcVkg/VtCpUqVwvfff2/UB2eWkpKCzZs3o3PnzpgyZYpS3qhRI5QpUwY//PAD6tWrB4DfEMoL7FPEYp8iFs9v8Zj5U5ZM0wcAsbGx2L17NwAY3e3t6ekJT09Po/chGafpA9LzjomJwZkzZ6BWqzlNXz5jHy4W+xOxmLdt44A5USHg4uKCjz76CCEhIShRogQuXLiA6dOnIyQkBBs3bkRYWBiqVasGANi7d6/R1//37dsHWZZx7949i17r9u3bKF26tPLzK6+8gl27dhktXhYREYF27dqhZMmSAICePXsarRi/evXqvGg2EdELo0iRInBxcTEqc3BwUP5lR6VSQZKkLM93dnaGSqUyer4l3xDatGmTVX1D6LWBVwu6CkY0Kj1aBSZj/Krr0Oqt58ucO+eVK+gqEBE9t5ym6QOAU6dOKdPqGQwcOBAAEBwcrHzLEDCeps/g6NGjRh8wA5ymj4joRcMBc6JCoHbt2qhdu7byc+PGjdGhQwfUqFEDo0aNQlhYGAIDA9GkSRNMnz4dVapUQYsWLfDvv/+if//+UKvVFs/55+HhgUOHDiE1NRWnT5/GV199haZNmyIqKkoZSDesGB8TEwNXV1d4eHjg/v37GD58OCIiIpQV42fOnInExESEhYVh7ty5cHNzy5d8yLpwcMsyHNx6sWT3DSEgfW7+ESNGYMmSJXjzzTeRkpKC6dOnIzExER9++GG2+y5SpAjefPNNrFy5EqGhoXjttddw//59jB07Fi4uLkbrV/AbQkREZM1yWoMCSF9nydI7Ok1t16ZNG0yYMMFo8Ds7XIOCiKjw4YA52SSdXsKBC6Wh0/PLW+a4urqibdu2WLBgAZKTk+Ho6Ii1a9eiR48eeOeddwAAdnZ2GDZsGHbs2IGEhASz+1Kr1ahatSrUajUkSVK+htioUSO0bNkSFSpUwLRp0zB79mzlOVwx/tllzJvyH/sTsZi3MUu+IQQAQ4cOhaOjIwYNGoTevXsDANzd3bFhwwY0atQo29dQq9VYsGABvv76a7z11lvQ69PnCS1Xrhx27txp1FfzG0LPj+e4WMxbLF6jiMfMxWLeYrEPF4vnt1jM27ZxwJxskgwJsQ+KFnQ1rJ7hjglJSr8A8fLywqZNm3D37l3cvn0bvr6+cHR0xLx58/D222+b3Y8kSXB1dTX5mLe3N8qUKYNz586ZfT5XjM+d7PKmvMf+RCzmbcySbwgBwLJly/DRRx9h8ODBaNWqFdLS0rBixQq8/vrr+OWXX5TtTJEkCd9++y1mzpyJiRMnonHjxkhKSsLcuXPRokULbNu2TakDvyH0/HiOi8W8xeI1injMXCzmLRb7cLF4fovFvG2bTQ6Yz5s3D9OnT8etW7fw0ksvISIiQlmMypTU1FSEh4dj1apVuH37Nry9vfHpp5+iZ8+eAmtNeUmj0qNFjSvYfsLXqqZQsCbx8fH4448/UKtWrSzz23p5ecHLywsAMGfOHDx69AiDBw82uy+tVovo6GjUrl07y1cTL1y4gOvXr6N9+/Ymn1sYV4yPiooymgc+o/379xst3PfkyRN88803WLZsGS5cuAB7e3tUq1YNM2bMQMOGDU3uw5D3iBEj8Oeff2Z5PCwsDFu2bFF+jo+Px8CBA7Flyxa4ublh9OjR6Nu3r9FzDh48iJCQEBw9ehQBAQHP0uxCi/2JWMw7Z5m/IZSSkqLcWT5jxgxlu1atWiEkJAT9+/fHpUuXzO7vxIkTGD9+PKZNm4ZRo0YZPb9atWoYPnw4du3apZTzG0LPh+e4WMxbrOyuCSl/FPbMrXGqPmvsUwrrVH3WmndhVdj7E2vDvG2bzR2xNWvWYOjQoZg3bx4aNWqEhQsXolWrVvj3339RrpzpPyLvvPMO7ty5gyVLlqBixYq4e/cutFqt4JpTXtOo9AVdBavRtWtXlCtXDkFBQfDw8MD58+cxc+ZM3Llzx2hRmsWLFwMA/P39kZCQgM2bN2PJkiX44osv8PLLLxvtM/OK8WfPnsUnn3yCjh07ws/PDyqVCidOnMDXX3+NEiVKYMSIESbrVphXjP/iiy+yDJxXr15d+X+dTocOHTrgr7/+wqhRo9CwYUM8evQIR44cMfrAwBTD4kN+fn744YcfjB7L/Cn1xx9/jOjoaKxatQrnzp3DgAEDEBAQoHyQqNVq0bdvX4waNYqD5WawPxGLeecs4zeEzp49i+TkZNStWzfLdkFBQdi9ezcePnxotPhyRsePH4csy8q3egyKFCmCwMBA7N6922w9+A2hZ8NzXCzmLVbmBRIp/zFzsdiniMW8xWJ/Ihbztl02N2A+a9Ys9OrVS5m/MyIiAlu3bsX8+fMxderULNtv2bIFu3fvRkxMDNzd3QEA5cuXF1llonxXs2ZNrFmzBgsWLMDDhw/h7u6OV199FStXrjQaYJFlGREREbhy5QpUKhVq166NX3/9Fa+//nqWfWZeMd7d3R2lS5fGzJkzcevWLWi1Wnh7e6Nt27YYO3YsfHx8suyjsK8YX6lSJaO7yTP75ptvsHnzZuzdu9douzZt2lj8Go6Ojtm+BgBs3LgRERERaNOmDdq0aYPNmzdj48aNyoD5jBkzkJqairFjx1r8ukRUcDJ/Q6hMmTIAgAMHDqB79+7KdrIs48CBA3Bzc4OTk5PZ/Rmef/DgQbz22mtKeWpqKo4ePQpvb2+TzyuM3xAiIiIiIiLKiU0NmKelpeHIkSMYPXq0UXloaCj27dtn8jnr169HUFAQvvrqK6xcuRJOTk5o3749Jk+eDEdHR5PPSU1NRWpqqvJzUlISgPS7NA13pqtUKqhUKuj1emXxrIzlOp3O6A2kuXLDIoqZ73g3LAqQ+dMoc+UajQayLBuVS5IEtVqdpY7myrNrEwCoVXpkXIpDJ0uQZSlruV6CDCnLJ8Xa/xby0KhkC8tVkCBDnaFcBqDTqyBJMiTIymsYylWSDJX0dHu9DOhlFVSSHqoMldTLEvSm6v4cbcp4DEUfpxEjRmDUqFEmzzG9Xq+U9+zZU5mKKOO5Z6ruT548SW+jVgudTgc3NzcsX75cmQ89c5sy7sNQ9ypVquDBgwfKfjK2aciQIRgyZIjZNmUst7bfJ0O54UMFc8dp9uzZaNKkCYKCgrL0Hdm1yfBYxseza1NKSgocHByg1WqhVqtRrFgxPH78GFqtFjExMZg8eTI2bdoEOzs7k8cpL/qI3BwnCbKQPkItZS031UcYtshYn/zoI3LbJsOxyovjpFHpraJNhrpIMNw9bflxyu++PPPvPCCmL+/SpQt8fHxQp04deHh44MKFC/j666+Vb8ZptVqUKVMGHTp0wKJFi2BnZ4eWLVsiNTUVK1euxN69ezF58mSjujg4OCA4OBg7duyATqdD/fr1ERAQgPDwcCQnJ6NJkyaIj4/Ht99+i0uXLinfRMrcpsmTJ8PBwQFDhw5VzsfmzZsr3xDy8/NDeHg4QkNDodVqC6wvBwznVv70Ebk99wAYXaMA4q6NsmtTXl3DGtpgDW1K358ehnNAxPWepW3Kz+vy7I5TZGQkmjdvDlP27NmjfAjfu3dvk1MpValSBadPn86235NlGY8fP8b8+fOxcuVKXLp0CcWKFUPt2rUxduxYNGrUSGnTvXv3MGTIEGzdulWZNq53795GbTp06BBee+01HDp0CFWrVs2xrbby/imv+j3DNWHGetp6mzLW3fD7lt/Xe5b25Rn7lIJ6n2uqTXl1nKypTSpJVq4L069Vxb13z6lcq9Xa5O9TTuVA1us9W2+TNR+njO/rC+q9Bo+TcXluZhuxqQHzuLg46HQ6lCxZ0qi8ZMmSuH37tsnnxMTE4K+//oKDgwN+/fVXxMXFYeDAgbh//z6WLl1q8jlTp07FpEmTspRHR0crd3B5enrC398fly5dQmxsrLKNt7c3vL29ce7cOSQmJirlfn5+8PLywsmTJ5GcnKyUV61aFa6uroiOjjY6cWrWrAk7OzscPnzYqA5BQUFIS0vD8ePHlTK1Wo26desiMTERZ86cUcodHR0RGBiIuLg4xMTEKOUuLi4ICAjAzZs3cf36daU8uzYBQF2/O/As/lgpP3bVE1fvOaNJlRso5pCmlB+4UBqxD4qiRY0rRn+kok77IDlNg1aBxvOsbj5WAY52WoQEXFPKtHoVNh+rAI/iyahf8ZZS/jDFDrtO+6C060Oo1TJa1LgCAIh9UBQHLpRGpVLxqFwqXtn+6r3iOHbVCzV84lCuxAOl/NxtN5y95Z6nbTp8+Ok5WFDHKT/PverVqyMtLU35Wn5haNOzHqezZ88CAAYMGIB3330XRYsWVeb3DQwMBJD+gcPly5fRpEkT9OrVCxs2bEBSUhL8/f0xduxY1KtXL9s26fV6PHz4EBcvXoS7uzuSkpJQqlQpNG/eHD169EC9evWUNlWrVg3Tpk2Dvb09nJycsHXrVowZMwaHDx/G0KFD0axZMwQHByMhIcEqjpNHcdd87yN83B8gsNzTOmbfR3jibpKT0p8A+dNH5LZNhj4lL45Tq8Bkq2iTQcoTDbR6CeVK5OY45W9frtPpCuRvrre3N3777TfMnz8fycnJcHV1RZMmTRAeHg4XFxelThEREWjYsCEWL16M7777DhqNBuXKlcPChQvRp08fHD582OjDvCdPnhi16euvv8b//vc//Pzzz5g5cybs7e1RoUIFzJo1C9WqVQMAozZdvnwZM2bMwO7du5GQkKC0yc3NDSNGjMDMmTMRHx+PoKAgvP/++zh8+HCB9eUaVal87iNye+4VR8oTjVGfIuLaKKc2GfqU5z1OrQKTraZNBneTnKDVSwgsF5vv13uWtqmgro0M6xn0798fderUga+vL4oXL45///0XaWlpyu+PVquFo6Mj5syZY9Sm2rVrIzk5Odt+T6/Xo1OnTti2bRvGjBmD2rVr4+zZs1i5ciVee+01/PDDD+jUqRNu3ryJ/v3748SJE/jss89w//59DBgwAM7Ozso3fjNOG6dWq41+vwv6es9armGLFCkCvV6P6OjoQtOmjMfJ0Kfk//We5X35udvu0OolNA24Lvx9rrk25dVxKoj37jkdJ8P7epHv3XNqU3R0rE3+PhmY6yOCgoJQoUIFo/7E1ttk7cdJr9cjLS0NkiQVmjYBtnuccpoaNyNJtqHv0d68eRNly5bFvn370KBBA6X8888/x8qVK42CNwgNDcWePXtw+/ZtuLi4AAB++eUXvP3223j06JHJu8xN3WHu4+ODe/fuwdnZGcCL92lN88HXC/yuRCDjp9R62Kv1/z1Xsoo7zDdFPP1Ku7V/qmaoo6XnnuF5hvoUhjZlV55Tm44cOYKVK1ciODgYJUqUwKVLlzB9+nScO3cO69evR2hoKP7++280atQIzs7O8Pb2xrhx4+Di4oIlS5Zg3bp1WLBggdG8vxnbZMhh4sSJ8PHxQUBAAB4+fIgtW7Zg0aJFaNCgAXbt2qVsf/bsWXTo0AHnz58HAHzwwQdYuHAhfvzxR4waNQonTpyAl5eX1RynsA9vWMVdiYChj5Bgp9ZBL6f3J+nlBX83tqFPyYvj1HrodatoU7r0/0/VqiFJsJo7zCO/LWc1f3Pzut+TZVn5tpFheixbb1NGoR/eSG+DldxhLsuAvUYHnf5pn1LQd/CppKd9yvMep9ZDr1tNmwylKklGmk79X/nTfRdkvxf5rU+B3mH+v//9D2+99ZbZ36fevXvj559/RkJCglF5Tv2eVqtFcnIy3N3d0blzZ6xatUqp461bt1CuXDkMGTIEc+bMgV6vV6bz69y5M1QqFVq2bInatWvj888/BwB89dVXWLFiBY4dO6a8Rk5ttZX3T3l5h/mTJ0+gUqmUb3raepsy1t3Qp1jLHeaADEkCnuhUUKtkq7kbe8dc7zw5Ti0GX7aaNqUfJ336t7X10n/HwzruMN8U4W2Tv0+WlGf8FmthaJM1HyfDNXiRIkUgSVKhaFNOdbf2NiUlJaFEiRJITExUxnfNsak7zD08PKBWq7PcTX737t0sd50blC5dGmXLllUGywEgICAAsizj+vXrqFSpUpbn2Nvbw97ePku5RqPJsrKt4QBkZjhJLC03t2JubsolSTJZbq6OuS3XmVm12ly5uVWuDX+QLCmXIZksV0tAixpXsPlYBaPXMfwxzUwvqzJMvZBz3Z+lTaayf9bjZI2rxbcKvJQl74JmbrX4/P59qlu3rtHc8CEhIejQoQNq1KiBMWPGoHXr1spjKSkp2LRpE3x9fQEALVu2RFBQEKZMmYJ+/fpl2b/hjVF0dDQmT55sVId27drBz88PI0aMwPr169GhQwdoNBq89NJLOHPmDGJiYuDq6goPDw/cv38fI0eOREREBLy8vDBv3jzMnDkTiYmJCAsLw9y5c+Hm5qa0KS/6CEv7PcNbj/zsI2RZgtZkX5C1j9Co9AirmbU/AQq237P0740l5RnrVdB9uUalR2iNy0relh6n9PL868vN/c4DBfM3Ny+vI7RaLaKjoxEUFKS8ObL1Nhkz9Cn500ekl1t+7qWf46b7lILs9/LqGtaa2gQY+vCnfUp+X+9ZWl7Qv09qtdro9fPq2kiSJBw/fhwqlUpZhNxQR3d3d6hUKuWGJJVKhZSUFDg7Oyv7K1asGFJTU6HRaBATE4PPP/8cmzZtMvneK6e22sL7p7zo93Q6ndKHZ37cVtuUsTxrn1KwfblGpUfLmtm/7ymIfi+vjlNBvHfP7jipJAkt/rsu1Muq/8oLvi83nJ+29vuUU7lWq8WRI0dM9ie22ibAeo9TxmtwU+OJmbfPyFrb9Dzl1tAmc/U1xXpGvixgZ2eHOnXqYPv27Ubl27dvR8OGDU0+p1GjRrh58yYePnyolJ07dw4qlcrsIldERLnl6uqKtm3b4vjx40hOTkaJEiUApH+FyTBYDqT/kQgLC8P169dx9+7dXL/Oe++9ByB98b+MVCoVKlasCA8PDwDp89obpomJjIzEJ598gjVr1uDChQuIjY3F0KFDn7GlREREZO0GDRoEjUYDZ2dnhIWF4a+//sqyTXJyMkqVKgW1Wg1vb28MHjwY9+/fz3HfGo0G/fv3x/fff4/ffvsNSUlJuHz5Mvr06QMXFxf06dNH2bZhw4aYO3cu7t69i71792Lr1q3K+7YBAwagc+fOCA4OzruGExEREeUBIXeYJyUl4cCBA7hx4waSk5Ph4eGBatWqoXr16rne1/Dhw9GtWzcEBQWhQYMGWLRoEa5evYr+/fsDAMaMGYMbN25gxYoVAICuXbti8uTJ+OCDDzBp0iTExcVh5MiR6Nmzp9lFP4mInoXhaz6SJMHf3x9FixbNdjtTn4paKrvnRkVFYc2aNcp885s3b0ZoaCiCgoIAAIMHDzaaDoaI8pZ1fksoGeNXXbeJbwkR0bNzcXHBRx99hJCQEJQoUQIXLlzA9OnTERISgo0bNyIsLAwAEBgYiMDAQOX92O7du/H1118jMjIShw4dQrFixbJ9nZkzZ8LNzQ1vvfWW8rXocuXKYefOnahYsaKyXUREBNq1a6d8G7hnz57o2LEjVq1ahX/++QerV6/OjxiIiIhyFBUVhaZNm5p8bP/+/cpC2RnJsozg4GDs2bMHgwYNwty5c7N9jUePHmHq1KmIjIzEmTNn8PDhQ1SoUAHvvfcePvroIzg4OCjbxsfHY+DAgdiyZYuyUHbfvn2N9nfw4EGEhITg6NGjCAgIeIZWk6XybcBcq9Xi559/xoIFC7B3717o9XqjuWQkSUKJEiXw7rvvYuDAgSanRjGlU6dOuHfvHsLDw3Hr1i1Ur17daLqDW7du4erVp29UixUrhu3bt2PIkCEICgpCiRIl8M4772DKlCl522AieqHFx8fjjz/+QK1atZQ/eq+//jp+/vlnXL58WVncSpZlbNmyBf7+/srd4Lnx/fffA4DJP95A+hoM/fr1w4QJE+Dn56e8ZsbFLR4+fGjUHxMREVHhULt2bdSuXVv5uXHjxsq0caNGjVIGzIcNG2b0vBYtWqB27dp4++23sXjx4iyPZzZ16lTMmDEDEydOROPGjZGUlIS5c+eiRYsW2LZtm1KHKlWqmJw2bvjw4YiIiIC7u3u208YRERHlty+++CLLwLm5G3y//fZbXLhwweJ93759G3PmzEG3bt0wfPhwFCtWDHv27MHEiROxfft2bN++XZky8eOPP0Z0dDRWrVqFc+fOYcCAAQgICEDjxo0BGC+UzcHy/JcvA+br16/HiBEjcPnyZbRo0QJffPEFXn75ZXh5ecHBwQH3799HTEwM9u/fj99++w1z585Fr169MGXKFIsGkAYOHIiBAweafGz58uVZyqpWrZplGheybVq99N+8cqbnH6O8xbyNde3aFeXKlUNQUBA8PDxw/vx5zJw5E3fu3DHqgyZPnozNmzejZcuWmDhxIpydnfHdd9/h2LFj+Omnn4z2qdFoEBwcjMjISKjVaqSkpKBt27bo0KED/Pz8kJKSgs2bN2PRokV47bXX0K5dO5N1+/zzz+Hg4IDhw4crZWFhYZg9ezbmzJmDihUrIjw8HC1btsyXbGwRz2+xmLd4zFws5i0W886ZYdq4BQsWIDk52ey3bDt06AAnJ6cs075lpFar4eTkhIkTJ+Krr77CiBEjlMdatWqFatWqYfjw4di1a5dSbpg2zsDUtHG7du1CxYoV8c4772Do0KHKDQKUnnlQUJDZuVkpb7FPEYt5i8X+xLRKlSqZvSEto8uXL2PMmDFYsWIF3nzzzRy3V6vVaNu2LV5//XWjb2699tprcHJywsiRI7F37168+uqrAICNGzciIiICbdq0QZs2bbB582Zs3LhRGTCfMWMGUlNTMXbs2GdsKeVGvgyYd+/eHcOGDUP//v3h5eVlcptXXnkFXbp0wZw5cxAZGYnPP/8c8+bNw/jx4/OjSlQIOdpp8SClSEFX44XBvJ+qWbMm1qxZgwULFuDhw4dwd3fHq6++ipUrVxotBurv7489e/YoX6V68uQJatWqhfXr16Nt27ZG+9TpdEYrRpcoUQJqtRqTJ09GXFwcJElCpUqVEB4ejo8//tjklCynT5/G9OnTERUVZbSYRWhoKKZPn46ZM2ciISEBoaGhiIiIyPtgbBjPb7GYt3jMXCzmLRbzzlnGaeNy2i6nKeOOHj0KWZaNrnkAoEiRIggMDMTu3bvNPpfTxj2btLQ0TicqEPsUsZi3WOxPnl3fvn3RokULdOjQweLnFClSxGTe9erVAwBcu3ZNKUtJSYGTk5Pyc7FixZCSkgIAiImJweTJk3NcKJvyTr4MmF+6dElZNd0SzZo1Q7NmzZCQkJAf1aFCSKOSERJwjZ9GC8K8jY0ePRqjR4+2aNvq1avjjz/+yHG7jFOk6HQ6PHr0CL///nuuVnEOCAhAcnKyyceGDRuW49erX1Q8v8Vi3uIxc7GYt1jMO2empo0z5eeff8bjx4+zvctOp9Ph8ePHANIXIM+4YGdqaiqOHj0Kb29vk8/ltHHPRqfT4fjx4wgKCsrVdSE9G/YpYjFvsdifmDZo0CB07twZRYsWRYMGDTBu3Djlrm+D7777Dn///Tf+/fdfi/ebXd47d+4EALz00ktKmWGh7Pr16+P8+fPYunUrli1bBoALZReEfFn5KTeD5XnxPCIiIiIiohdd165dMXr0aPz888+IiorC4sWL0aBBA9y5cwfTp08HAFy5cgWNGjXCN998g82bN2PLli0YM2YMunfvjpdeegm9e/c22qdGo0GzZs2UnwMDAxEUFISJEydiwoQJiIyMxC+//IKWLVvi0qVL+Pjjj03Wzdy0cTt27MCcOXOwadMmm5s2LioqCpIkmfxnmNpGp9Nh1qxZaNmyJby9vVG0aFEEBARg9OjRFt0wdvnyZTRo0ABFihQx+ToZ84qPj0eXLl3g5uYGPz8/LFq0KMv+Dh48CEdHR5w+fTrPciAiskWGhbIXLlyIXbt2Yfbs2bh27RpCQkKwdetWZbsbN25gxIgR+Oqrr1CmTJnnft3jx4/jq6++QocOHVCzZk2lPCIiApcvX0bJkiXx6quvonPnzkYLZRv+jpMYwj9SunDhArZv3w5ZltGsWTNUqVJFdBWIiIiIiIgKHUumjXN2dkbJkiUxa9Ys3LlzBzqdDr6+vvjwww8xduxYo6+DA1mnjVOpVNiyZQu+/vprrF27FjNmzECxYsVQrVo1bNq0Ca1atcpSr8I+bVx2C8YlJydj4sSJ6NKlC3r37g0PDw8cPXoUU6ZMwYYNG3D48OFsp0coXbo0Fi9ejKpVqxpl99tvv+HLL780mhqAC8YR2baoqKgsfYnB/v37Ub9+feh0OsyePRvbtm3DyZMncf/+ffj6+uL111/H6NGjc30janJyMgIDA3H+/HlMnz7daG2K+Ph4DBw4EFu2bIGbm5sy1WhGBw8eREhICI4ePWpz/YqlC2X3798fgYGB6NOnz3O/5uXLl9G2bVv4+Pjgu+++M3qMC2VbF6ED5r/99hu6dOmCatWq4eHDhxg2bBhWrVqFjh07iqwGFRJafb58QYLMKMx5vzbwakFXwYhGpUeLGqkYv+q6VeW+c165gq5CvrGmnF8EzFs8Zi4W8xaLeT9lybRxbm5u+OWXXyzeZ+YpUtRqNVxcXDBlyhRMmTLFon0U9mnjslswztHREZcuXUKJEiWUspCQEJQrVw4dO3bEunXr8N5775ndt729PQIDA1G7dm2jAfMxY8agaNGi6NKli1LGBePyBvsUsZh3Vvn5IVzmBT/HjRtnNDVWRi/ih3CZF8reuHEjtmzZgr/++guJiYlG26alpSEhIQFOTk4oUsT0PPwZ875y5QqaNm0KjUaDyMhIuLu7Z9meC2VbD6ED5p999hl++eUX5a6Dzz77DJ999hkHzCnXtHoVNh+rUNDVeGEwb7GYt1jMWyzmLR4zF4t5i8W8xdJoNFkW+6TsqdVqo8FyA1MLvpliKvOLFy9i9+7d6N69O5ydnZVyLhj3/NiniMW8TcuvD+Ey9yd///03vvnmG/zwww8mx+Ve1A/hMi6UffLkSWi1WpPHY/HixVi8eDF+/fVXvPHGG1kez5j3lStXEBISAlmWERUVZXa9j4y4UHbBypeP8nr06IH79+9nKb9165bRp2TBwcG4fft2flSBCjkJMjyLP4YELgokAvMWi3mLxbzFYt7iMXOxmLdYzFssWZaRkJDAhTkzGTRoEDQaDZydnREWFoa//vorx+eYWvDNFFOZL126FLIsZ5lv3rBg3N27d7F3715s3boVDRs2BMAF4yzFPkUs5p17z/MhXMb+JC0tDT179sSgQYOUAdjMLPkQbuHChYXqQ7jMC2X36NEDu3btyvIPAN544w3s2rUrywKhBoa8DYPlOp0OO3fuhK+vb4714ELZBS9f7jCPi4tD1apVMXPmTHTr1k0pb9iwIYYMGYJhw4bh4cOHmDJlCho0aJAfVaBCTq2SUb/iLa6mLQjzFot5i8W8xWLe4jFzsZi3WIU9b2ucNq5V4KX/8raeaRQKato4w4JxISEhKFGiBC5cuIDp06cjJCQEGzduVOa/zezGjRsYPXo0goKC0LZt22xfQ6fT4cyZMwgKCoJGo4FOp8P333+PqlWrolGjRkbbRkREoF27dihZsiQAoGfPnkYLxq1evTpvGl6IFfY+xdowb9MGDRqEzp07o2jRomjQoAHGjRtndlDWwJIP4TL2J+Hh4Xj06BEmT56M2NhYk9sbPoSrX78+zp8/j61bt2LZsmUACseHcF27dkW5cuUQFBQEDw8PnD9/HjNnzsSdO3ewfPlyAED58uVRvnx5k88vW7YsQkJCjMo0Gg2Cg4MRGRkJnU6Hffv2YciQIbh16xaWLFmCu3fv4u7du8r23t7eJu82N7dQ9uzZszFnzhxUrFjR5hbKtjX5MmD+xx9/4KefflLm0lm0aBH8/Pzw7bff4t1331XmXmrQoAEWLFiQH1UgIiIiIiIiyjeWLhiX0f3799G6dWvIsow1a9ZApcrdBw9btmzBjRs3MH369CyPccE4Itsm4kM4APjnn3/w1VdfYcOGDXBycjI7YF7YP4SzZKHs3Mq8UPbly5cRExMDACanypkwYQImTpxoVFbYF8q2Ffk2h/k777yDsLAwjBo1CjVr1sSnn36KUaNGYc+ePXj06BFkWUaxYsXy6+WJiIiIiIiIhMq8YFzGxffi4+PRokUL3LhxAzt37lS+Zp8bS5YsQZEiRfD++++bfJwLxhHZLhEfwmm1WgwYMACdOnUyOwBvUNg/hLNkoWxzzE2Fkrn85ZdfxpMnT4wGvnNS2BfKthX5+j06FxcXLFy4EFu3bsUPP/yAWrVq4cCBA3BycuJgOT0XGcDDFDvOdCYI8xaLeYvFvMVi3uIxc7GYt1jMWyzmbZmMC8YZxMfHo3nz5rh06RK2b9+OmjVrWrQvSZLg6OgISZJw9+5d/PHHH2jfvj28vLxyfK5hwbj58+cDMF4wztXVFYMHD8amTZueoYWFF89xsZh3zgwfwh0/fjzLIGrGD+G2b9+e44dwkiThl19+waVLlzBhwgQkJCQgISEBSUlJANLnLE9ISDC6Q9rwIZyHhwcA0x/CrVmzBhcuXEBsbCyGDh2atwHYsIz9N9mefB0wT01NRVJSEho1aoTo6Gi88847aNasGQYNGoQHDx7k50tTIafTq7DrtA90VjR3YmHGvMVi3mIxb7GYt3jMXCzmLRbzFot55yzzgnGGsubNmyMmJgbbtm0zuoM0J2q1GoGBgVCr1VixYgWePHmCXr165fg8Lhj3bHiOi8W8LZNXH8Kp1Wrcv38fiYmJqFSpEtzc3ODm5obAwEAAwLhx4+Dm5oYTJ06YfD4/hMudjP032Z58mZLl5s2b6NGjB3bu3AlZlhEQEIBly5Zh3Lhx6Ny5M/r374+qVavim2++wZtvvpkfVaBCTpJk+Lg/wLX7xSHL/LQuvzFvsZi3WMxbLOYtHjMXi3mLxbzFYt7GLFkwLjk5GWFhYYiOjkZERAS0Wi0OHDig7MPT0xP+/v7KzxkXjAMAvV6PuLg4eHh4YMmSJfDx8clxGgWAC8Y9K57jYjHvnOX0Idz27dst/hBOr9ejT58+eP/9942mbrl9+za6dOmC/v37o1OnTkbTOhnYyodw1rRYtrWe3wW1ULatyZcB8379+uHBgwfYs2cPnJycMH36dHTo0AFXr15FpUqVEBkZiWXLlqFfv374/vvv8fvvv+dHNagQU0syAsvF4mZ8MWitqOMprJi3WMxbLOYtFvMWj5mLxbzFYt5iMW9jliwYd+fOHRw6dAgA8NFHH2XZR/fu3ZXBdSDrgnF6vR4xMTE4c+YMzpw5g/Hjx+c4RzEXjHt2PMfFYt7G8vtDOL1eD7VajSZNmhj1DZcvXwYA+Pv7IyQkxGTd+CFc7vH8tm35MmD+559/Yt26dWjQoAEAYPbs2fDw8MDFixdRqVIlAMAHH3yAtm3b4uOPP86PKhARERERERHlG0sWjCtfvnyu7rg0t23Dhg0t3g8XjCOyTSI+hHsW/BCOXkT5MmBeunRp7NmzB82bNwcA7N27F5IkoWTJkkbbeXp6YsWKFflRBSIiIiIiIiIiIpsg8kO43OyTH8LRiyhfBsy/+OILdOnSBT/99BOcnJxw7NgxfPrpp3B2ds6Pl6MXkAwg9kFRrqYtCPMWi3mLxbzFYt7iMXOxmLdYzFss5i2eJElwcXExWuyP8g/PcbGYt1jsT8Ti+W3b8mXA/M0338Tp06exbds2pKSkYN68eahXr15+vBS9oHR6FQ5cKF3Q1XhhMG+xmLdYzFss5i0eMxeLeYvFvMV6EfK2pgXjnnICcKOgK6EozAvGvQjnuDVh3mKp1WoEBAQUdDVeGDy/bVu+DJgDgJ+fH/r3759fu6cXnEqSUalUPM7fdoOeiyfkO+YtFvMWi3mLxbzFY+ZiMW+xmLdYzFs8Zi4W8xarsOdtbR/AWWvehfVDOGvNmyyT/fLaz+jatWvP9LwbN6znU3OybipJRuVS8VBJ/HKLCMxbLOYtFvMWi3mLx8zFYt5iMW+xmLd4zFws5i0W8xaLeYvFvG1bvgyYV6pUCR999BHOnz+f47ZPnjzB2rVrUatWLSxdujQ/qkNERERERERERERElKN8GTDfvn079u7di6pVq6J+/foYM2YM1q5di927d+PgwYPYvHkz5s6di/feew+lS5fG+++/j5YtW1q8su68efNQoUIFODg4oE6dOtizZ4/ZbaOioiBJUpZ/Z86cyavmEhEREREREREREVEhkC9zmDdu3BiHDx/G5s2bsWDBAsyZMwfJycnKSryynP51BD8/PwwaNAj9+/dH6dKWTYS/Zs0aDB06FPPmzUOjRo2wcOFCtGrVCv/++y/KlTM/79HZs2fh7Oys/Ozp6fkcLaSCppeBq/eKQ89vtgjBvMVi3mIxb7GYt3jMXCzmLRbzFot5i8fMxWLeYjFvsZi3WMzbtuXbop8A0KpVK7Rq1QpPnjzBP//8g5s3byI5ORkeHh4ICAhA2bJlc73PWbNmoVevXujduzcAICIiAlu3bsX8+fMxdepUs8/z8vKCq6vrszaFrIxeVuHYVa+CrsYLg3mLxbzFYt5iMW/xmLlYzFss5i0W8xaPmYvFvMVi3mIxb7GYt23L1wFzgyJFiqBu3brPvZ+0tDQcOXIEo0ePNioPDQ3Fvn37sn1u7dq1kZKSgmrVquGzzz5D06ZNzW6bmpqK1NRU5eekpCQAgFarhVarBQCoVCqoVCro9Xro9XplW0O5TqdT7qTPrlytVkOSJGW/GcsBQKfTWVSu0Wggy7JRuSRJUKvVWeporjy7NgGAWqVHxnV9dbIEWZayluslyJCgUemRkVafvpVGJVtYroIEGeoM5TIAnV4FtUqHmj6xOHXdA3pZpZSrJNloQQW9nN5JqSQ9VBkqqZcl6E3V/TnalPEYPu9x0qj0VtEmA5WkR0DZezhxzQMqScq0vfnjJEky1FLW8rxqU179PmXNoODapFbpoZb0eMk7Dqeue+CJTp3vv08Wt0mvz5N+T4JsPW36739r+sTi3xsloJdV/5Xn3++TpW0ynMd50ZdrVHqraBOQ3p+85H0Px656QoaU779PlrYpc98MPHtfnnH/1tCXqzL0KWk6tVJuvL34fs/QpzxvX55eI+tok06WIEFGYLlYnLr+tE8RcW2UU5vy6hrW0AZraBOQ3qdUK3sPx695/vfz030XZL+XV9flIq/3cmqTBBlF1DqlP9HJKmHXRjm1SavV5tn7p/T/FnybgPTjoZL0//UpHkqfkt/XRjm1KWP//Lzvcw1tLug2GYoz9imSBOHvc821KTfv3bPrywvivXt2x0mjetqnaPUq4e9zzZVrtdq8GWOR9FbTpvTXBWr4xOJ0hvc9It/nmmuTJWMplvblGcdSCrovN1yDH7vqCb2sEv4+11ybDDkWpjFLS8cjMrcjO0IGzPNKXFwcdDodSpYsaVResmRJ3L592+RzSpcujUWLFqFOnTpITU3FypUr0axZM0RFRaFJkyYmnzN16lRMmjQpS3l0dDScnJwApE/p4u/vj0uXLiE2NlbZxtvbG97e3jh37hwSExOVcj8/P3h5eeHkyZNITk5WyqtWrQpXV1dER0cbnTg1a9aEnZ0dDh8+bFSHoKAgpKWl4fjx40qZWq1G3bp1kZiYaDQ3u6OjIwIDAxEXF4eYmBil3MXFBQEBAbh58yauX7+ulGfXJgCo63cHnsUfK+XHrnri6j1nNKlyA8Uc0pTyAxdKI/ZBUbSoccWoQ4867YPkNA1aBV4yatPmYxXgaKdFSMA1pUyrV2HzsQrwKJ6M+hVvKeUPU+yw67QPyrk/RJ0KsfAp8QiyDMQ+KIoDF0qjUql4VC4Vr2x/9V5xHLvqhRo+cShX4oFSfu62G87ecs/TNh0+/PQcfN7j1Cow2SraZCBJAGQZV+Jc0LjKDYuPk4/7AwSWe3ou5XWb8ur3KWNbC7pNTarcQHHHNHg5P4ZPiUfYfz7/f58sbdOlS7o86fc8irtaTZuu3iuOU9c98JL3PZTzeAjD39f8/H2ytE2GPiUv+vJWgclW0SYgvT9xd0rGiWueKOOW/79PlrZJp9Pl2d/cjNlYQ18uSVD6lE3/5O/vU27aFBdXNE+ujTSqUlbTpmNXPXEzvhgCy8XCp8TTPkXEtVFObTL0Kc97DdsqMNlq2gSk9ynFHVJx8ronXvIWd22UU5vy6rpc5PVeTm3yKJ6MBpVuKf3Jg2Rx10Y5tenw4dt59v4JcLOKNhmO0xOtyuh9jyXHKb/7vYzve573fa6hTynoNhmOkyQBDkW0OHndE40L4H2uuTbl5r17dn15Qbx3z+44VSkdr/QpV+LEv88116bo6Ng8GWOp4aOxmjZp9SpsP+GLiiUT4ZvhfY/I97nm2pSxT3nevrxVYLJVtEmj0ivX4JdiXfEwxU74+1xzbQJ8C92YpaXjEY8ePYKlJDnj8LuVu3nzJsqWLYt9+/ahQYMGSvnnn3+OlStXWryQZ7t27SBJEtavX2/ycVN3mPv4+ODevXvKPOgv2h3mzQdfL/C7EoGnn54VUevQquYlbDtRPv0uHhT8HXybIryV8uc9Tq2HXreKNin1VunRosZlbD5WAcb3WRTs3djbvymbJ79PYUOuWE2b1Co9iqj0CK1xGdtOlEeq1nruMN8yp1ye9HthH96wmjbp5fS7EVsFxmD7f/1JennB341t6FPyoi9vPfS6VbQp/fH083vTMT/oZOu5wzzy23J59je35YdXraJNQPrx0GToU1K01nOHuaFPed5ro9APb1hNmwzndOvAGOUaBSj4O/hU0tM+5XmvYVsPvW41bUrfn+EaxU/p0w0Kst+L/NYnT67Lmw+6bDVtkiDDXqNT+pMneuu5w3xThHeevX9qMeSGVbQJSD8eGpUerQMvGfUpBX039uYM73ue932uoU8p6DYZijP2KTKs5w7zHXO98+ROyxaDL1tNm1SSDDv10z4lTWc9d5hvivDOkzGWlh9dt5o2GWR+3yPyfa65NlkylmJpX55xLKWg+/Kn73sqQKu3njvMd3zrW+jGLC0dj0hKSkKJEiWQmJhotM6lKTZ1h7mHhwfUanWWu8nv3r2b5a7z7NSvXx+rVq0y+7i9vT3s7e2zlGs0Gmg0xpEZDkBmhpPE0vLM+32WckmSTJabq2Nuy3X6rGXZlWvNlksWl8uQTJfL0n+PqYxex9ChZaaXVTC10EJetslU9s96nKylTZlqDvyXeWbZHSetybrnTZvy6vfJmtqk06sgKa+vgvzfn+D8/n2ypE2G8/N5+z1rahOQ/lU5mOhPgILt9yz9e2NJecZ6WUNfbjgHRPw+ma5j1nJzfTOQ+77c1P4Lui+Xlf5bMirPSHS/p0z59tx9ufW0Kb06sslrlPQ6Fly/l1fXsNbUpqek/8pFXxuZL8+r63JralPGaxPtf4PlgHX05Rmzzou+3BralKGW2fQpBdPvmcrsWftya2mTMclMuaGO4vu93PYd5o6HNbVJLxv3KU+nRiz4fs9wfj5vX25NbQIMUyCZ7k8Kst+zdCzFXHnG45SxXdbQl8v/jaPAzO9H+vbi+73COGZpSZvM1dcU00fXStnZ2aFOnTrYvn27Ufn27dvRsGFDi/cTHR2N0qVL53X1SCC9LOHcbTeTHQLlPeYtFvMWi3mLxbzFY+ZiMW+xmLdYzFs8Zi4W8xaLeYvFvMVi3rbNpu4wB4Dhw4ejW7duCAoKQoMGDbBo0SJcvXoV/fv3BwCMGTMGN27cwIoVKwAAERERKF++PF566SWkpaVh1apVWLduHdatW1eQzaDnpJclnL3lXtDVeGEwb7GYt1jMWyzmLR4zF4t5i8W8xWLe4jFzsZi3WMxbLOYtFvO2bfl+h/m6deuM5pZ5Xp06dUJERATCw8NRq1Yt/Pnnn9i0aRN8fX0BALdu3cLVq0/nDk1LS8OIESNQs2ZNNG7cGH/99Rc2btyIN998M8/qROKpVXrUr3gLalXenVtkHvMWi3mLxbzFYt7iMXOxmLdYzFss5i0eMxeLeYvFvMVi3mIxb9uW73eYd+zYEWXLlkX//v3Rp08feHl5Pfc+Bw4ciIEDB5p8bPny5UY/jxo1CqNGjXru1yTrIgHwLP4Y/GKLGMxbLOYtFvMWi3mLx8zFYt5iMW+xmLd4zFws5i0W8xaLeYvFvG1bvt9hHhUVhQYNGmDSpEkoV64cunXrhgMHDuT3yxIRERERERERERER5Uq+D5g3adIEP/30E65cuYJRo0YhMjISjRo1Qp06dbB8+XKkpqbmdxWIiIiIiIiIiIiIiHKU7wPmBqVLl0Z4eDiuXr2KVatWQaVSoVevXvD29saYMWNw69YtUVWhQkAnSzh21RM6rjYsBPMWi3mLxbzFYt7iMXOxmLdYzFss5i0eMxeLeYvFvMVi3mIxb9smbMDc4NKlSzh48CDOnz8PtVqNGjVqYPbs2ahcuTI2bNggujpko2RZwtV7zpDZ8QjBvMVi3mIxb7GYt3jMXCzmLRbzFot5i8fMxWLeYjFvsZi3WMzbtgkZMJdlGevXr0dYWBgCAgLw448/YvDgwbh8+TJ27tyJy5cvIyQkBMOGDRNRHSoE1Co9mgZc42rDgjBvsZi3WMxbLOYtHjMXi3mLxbzFYt7iMXOxmLdYzFss5i0W87Ztmvx+gS+//BILFizAlStXEBgYiMWLF6Nr166wt7dXtvHy8sLIkSPRtGnT/K4OFRISgGIOaVxtWBDmLRbzFot5i8W8xWPmYjFvsZi3WMxbPGYuFvMWi3mLxbzFYt62Ld8HzD/77DO0b98ey5cvR3BwsNnt/P39MX78+PyuDhERERERERERERGRSfk+YH7hwgX4+vrmuF3ZsmUxYcKE/K4OEREREREREREREZFJ+T6HeZkyZfDo0SOTjz169AhPnjzJ7ypQIaTTSzhwoTR0en65RQTmLRbzFot5i8W8xWPmYjFvsZi3WMxbPGYuFvMWi3mLxbzFYt62Ld/vMO/Tpw9SU1OxevXqLI/17dsXjo6O+O677/K7GlTIyJAQ+6BoQVfjhcG8xWLeYjFvsZi3eMxcLOYtFvMWi3mLx8zFYt5iMW+xmLdYzNu25fsd5rt27UL79u1NPtauXTtERkbmdxWoENKo9GgVeAkarjYsBPMWi3mLxbzFYt7iMXOxmLdYzFss5i0eMxeLeYvFvMVi3mIxb9uW7wPmd+7cQenSpU0+VqpUKdy+fTu/q0CFFDsdsZi3WMxbLOYtFvMWj5mLxbzFYt5iMW/xmLlYzFss5i0W8xaLeduufB8wd3V1xYULF0w+duHCBRQvXjy/q0BERERERERERERElKN8HzBv2rQppk6divv37xuV379/H9OmTcNrr72W31UgIiIiIiIiIiIiIspRvi/6OXHiRNStWxeVKlVCp06dULZsWVy/fh1r167FkydPMGnSpPyuAhVCWr2EqNM+0HK1YSGYt1jMWyzmLRbzFo+Zi8W8xWLeYjFv8Zi5WMxbLOYtFvMWi3nbtnwfMK9SpQr27NmD4cOHY/HixdDpdFCr1QgODsasWbNQpUqV/K4CFVLJafl++lIGzFss5i0W8xaLeYvHzMVi3mIxb7GYt3jMXCzmLRbzFot5i8W8bVe+T8kCAIGBgYiMjERSUhKuX7+OBw8eYMeOHahZs6aIl6dCSKOS/1ttWC7oqrwQmLdYzFss5i0W8xaPmYvFvMVi3mIxb/GYuVjMWyzmLRbzFot52zahH3U4OjrC0dFR5EsSEREREREREREREVlEyIC5TqfD5s2bcfr0aSQnJxs9JkkSxo0bJ6IaRERERERERERERERm5fuA+b1799C4cWOcOXMGkiRBltO/iiBJTye954A5ERERERERERERERW0fJ/D/NNPP4WDgwOuXLkCWZZx8OBBnD9/HsOHD0flypVx9erV/K4CFUJavYTNxypwtWFBmLdYzFss5i0W8xaPmYvFvMVi3mIxb/GYuVjMWyzmLRbzFot527Z8HzCPjIzE8OHDUaZMmfQXVKng7++P6dOno3nz5hgxYkSu9zlv3jxUqFABDg4OqFOnDvbs2WPR8/bu3QuNRoNatWrl+jXJ+jjaaQu6Ci8U5i0W8xaLeYvFvMVj5mIxb7GYt1jMWzxmLhbzFot5i8W8xWLetivfB8yvX7+O8uXLQ61WQ6VS4dGjR8pj7dq1w/bt23O1vzVr1mDo0KH49NNPER0djcaNG6NVq1Y53qmemJiI999/H82aNXumdpB10ahkhARc42rDgjBvsZi3WMxbLOYtHjMXi3mLxbzFYt7iMXOxmLdYzFss5i0W87Zt+T5g7uHhgcTERABAmTJlcPLkSeWx+/fvQ6vN3acts2bNQq9evdC7d28EBAQgIiICPj4+mD9/frbP69evH7p27YoGDRrkvhFEREREREREREREVOjl+4B5nTp1cOrUKQBA69atER4ejlWrVuGnn37C2LFjUb9+fYv3lZaWhiNHjiA0NNSoPDQ0FPv27TP7vGXLluHixYuYMGHCszWCiIiIiIiIiIiIiAo9TX6/wODBg3Hx4kUAwOTJk3HgwAG8//77AAB/f3/Mnj3b4n3FxcVBp9OhZMmSRuUlS5bE7du3TT7n/PnzGD16NPbs2QONxrLmpqamIjU1Vfk5KSkJAKDVapU74lUqFVQqFfR6PfR6vbKtoVyn00GW5RzL1Wo1JEnKcqe9Wq0GAOh0OovKNRoNZFk2KpckCWq1OksdzZVn1yYAUKv0yLhUgU6WIMtS1nK9BBkSNCo9MjIsdJD56yjmy1WQIEOdoVwGoNOrIEkydPqnr2EoV0kyVNLT7fUyoJdVUEl6qDJUUi9L0Juq+3O0KeMxfN7jpFHpraJNSr1V+v/KZRPbZ3+c1FLW8rxqU179PllTm9QqPTQqvXJ+i/p9sqhNen2e9HsSZOtpk/z0NTLWJz9/nyxtk+E8zou+XKPSW0WbDHXR/betiN8nS9uUuW8Gnr0vz7h/a+jLM/Yp+f37lJs2GfqU5+3L02tkHW3Syek/6TL1KdbQl+fVNayhDdbQpvT9PT2vRV4b5dSmvLout6Y2STDuT0ReG+XUJq1Wm2fvn9L/W/BtAgzHQ87SpxR0X56xf37e97mGdhV0mwzFGfuUgnqfa6pNuXnvnl1fbk1tUknGfUpBvM81V67VavNmjEXSW02bMj5m6lq1IPs9S8ZSLO3Lrenv09P3PTLSx1LEvs811yZDjoVpzNLS8YjczHKS7wPmzZs3R/PmzQEAnp6eiI6OxsmTJyFJEqpWrWrxIHZGhgsZA1mWs5QB6Qeoa9eumDRpEipXrmzx/qdOnYpJkyZlKY+OjoaTkxOA9Lb4+/vj0qVLiI2NVbbx9vaGt7c3zp07p0xFAwB+fn7w8vLCyZMnkZycrJRXrVoVrq6uiI6ONjpxatasCTs7Oxw+fNioDkFBQUhLS8Px48eVMrVajbp16yIxMRFnzpxRyh0dHREYGIi4uDjExMQo5S7/Z+++45q62jiAPzdBFAREAUEFFQeKA1BcOHAP3FTrtm5r7XAP3Fqt1kldqK3WbV2tWkerrXvVPat1oSiiAiIIIhjye//gzTUR0ADhJMHn+/m8n7ee3IRzfvfmyb0nN/cWKECenp70+PFjevTokdz+vjEREVUv9ZScbF/J7ZfDnCgs2o78y4WTTb5kuf30nSIU+dKamlZ+oFOID99wo8RkCwrwDtUZ077L7mRlqaIGng/lNpVaQfsuu5OjbSLVKhMht8e/tqRDN9yoiH0CKRWgppUfEBFR5EtrOn2nCJV1iSEPlxh5+bBoW7ocVpgqu0VRcYeXcvutJwXpv4hCBh3TuXNvv7TJ7noK8E40iTFldz25FXpJ3sXfbkuGHpOh3k/aYzX2mLTXU9PKD0xqPYWGphik7jna2pvMmDTr6XGMjVxPiEzj/aSpKYao5QHeiSYxJu31pFIrqLhDnLD304fGlJKSYrDPXO1sTKmWN638wKRqeVSUtUH2jSwULiYzJs16SkzOo1NTTKGWa2pKdvdhA7wTTWZM2utJpVaQd/FnJrNvZKj9clPd32ta+YFR9o0yGtO5c08MdvxEVNAkxqS9nrSPezKznohypu5pH/dk9zhXU1OMPaZ315NKraCGng9N5vgpM8fu76vlpnpM2LTyA5M6zr14MdIgcyyV3SxMZkya9XTunotOPTGFWq5dU7JbywO8E01iTNrrycoyhRKTJZOZYyEqkevmLPWdj9C+r+aHSNCefjewxMREatKkCU2dOlWeNM+O5ORksra2pq1bt1JgYKDcPmTIELp06RIdOXJEZ/kXL15QwYIF5W84iFLPZgJASqWS9u/fT40aNUrzd9I7w9zNzY2io6PJzs6OiD6+M8ybfPXI6GclEml/e6amwnavKPqlFYEkkziDb2+wq9ye3fXUcugjkxiT3EcC2ed/TVEvrUj5zoWcjHk29oFFxQzyfmr+9QOddmOfYa4gkINtIkW/tEo9S80EzuAjIvpjYXGD1L3m34SbzJjUIAIkKmz3ip7H5yP8f60Y+ww+pVZNMUQtbzn0kUmMiSi1nhSySaRncfmJJDKZM/j+XlLcYJ+5Lb55eyNyU6jlklZNeaNWyO26y4uve5qakt19o2bfhJvMmFIgEYGosF0CPY+3kmuKsc/gU0hva0p292FbDn1kMmMi0tSU1/QszpokCSZxBp9KraC/l7gZZL+8yZf3TWZMmjPMNfVETZJJnMFHRLQ32NVgx09Nvw43iTERpa4PiUDOBd4e92jaiYxX9/ZpHfdk9zhXU1OMPSZNs0SggvlfU+RLa1IokKPvp8yM6a/FrgY507LpV/dNZkwKCaSU3taUFEgmczb23mBXg8yxtBjyyGTGpHkdJ9tXFJPw9rhH5HFuRmPSZy5F31quPZdi7Fqu2Qd/Gmv9/3axx7kZjemvJSVy3ZylvvMRcXFx5ODgQLGxsfL8bkZy9AxzKysrunr1apbOIk+PpaUl+fr60oEDB3QmzA8cOEDt2rVLs7ydnR1dvXpVp23p0qV08OBB2rZtG7m7u6f7d/LmzUt58+ZN025hYZFmLJoV8C7tSXp92jPKKDPtkiSl255RHzPbnqJO/5L3GbWrMmxP+2uAjNpBUrrtCin1jPd9l911/o6moL1LDQWpkabZoGNKL/usridTGZOGhUJNtco8SZO3RkbrCZBIlW7fDTMmQ72fTGlMKWoFSQq1vH2/PTDKufeTvmPSbJ/ZrXumNCai1O27Run0t29j1j19P2/0adful7FreWreb+t3Tr+f0u9j2vaMajNR5mt5eq9v1M8nrZpC70y2aBNd9+RLvmW7lpvOmIjSbuPajFn3DLUPa0pjIkpbw0XtG32o3VD75SL39z7Unvr1myTXE83fErFvlH4f37ZrZ22IWm4KY9JQKpDucU/q8sape+llltVabipjkvulUFPN9xz3pNfn9/U9o/bMjimztSOj9WGMY/f3rSeF9LamqKH4f7vx655m+8xuLTelMRG9f/s2Zt3Tdy4lo3bt9aQ9LmPXcu19cJU6/fdH6vLi615unLPUZ0yZmZ/O8Uuy+Pn50ZkzZ6hBgwYGeb3hw4dTz549qVq1auTn50crVqygsLAwGjRoEBERBQUFUXh4OK1du5YUCgVVqlRJ5/mFCxemfPnypWlnjDHGGGOMMcYYY4wx9nHL8QnzefPmUbt27cjFxYU++eQTsrGxydbrde7cmaKjo2natGkUERFBlSpVor1791KJEiWIiCgiIoLCwsI+8CqMMcYYY4wxxhhjjDHGmC4hZ5gnJydTnz59qE+fPmRtba1zg05JknQuyq6PwYMH0+DBg9N9bPXq1e997pQpU2jKlCmZ+nvM9IBSb3qQzi9LWA7gvMXivMXivMXivMXjzMXivMXivMXivMXjzMXivMXivMXivMXivM1bjk+Yd+jQQWeCnDFDSFEr6NANN2N346PBeYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvFeZu3HJ8w/9AZ34xlhSSB3Aq9pIfPbQnp3NiAGRbnLRbnLRbnLRbnLR5nLhbnLRbnLRbnLR5nLhbnLRbnLRbnLRbnbd7Sv6UrYyZOKYG8i0eSUuIft4jAeYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvFeZu3HD/DfO3atR9c5rPPPsvpbjDGGGOMMcYYY4wxxhhj75XjE+a9e/dOt137uuY8Yc4YY4wxxhhjjDHGGGPM2HJ8wjw0NDRNW1RUFO3cuZM2b95Mv/zyS053geVCIKLIl9Z8t2FBOG+xOG+xOG+xOG/xOHOxOG+xOG+xOG/xOHOxOG+xOG+xOG+xOG/zluMT5iVKlEi3zdfXl968eUM//PAD3xiUZVqKWkGn7xQxdjc+Gpy3WJy3WJy3WJy3eJy5WJy3WJy3WJy3eJy5WJy3WJy3WJy3WJy3eTPqTT8bN25Mu3btMmYXmJlSSKByRZ6Tgm+eIATnLRbnLRbnLRbnLR5nLhbnLRbnLRbnLR5nLhbnLRbnLRbnLRbnbd6MOmH+4MEDUiqVxuwCM1MKCeThEsOFRxDOWyzOWyzOWyzOWzzOXCzOWyzOWyzOWzzOXCzOWyzOWyzOWyzO27zl+CVZjh49mqYtKSmJrly5QjNnzqTGjRvndBcYY4wxxhhjjDHGGGOMsQ/K8QnzBg0akCRJOm1A6rcrTZo0oUWLFuV0FxhjjDHGGGOMMcYYY4yxD8rxCfNDhw6lacuXLx+VLFmSnJ2dc/rPs1xKDaKwaFtS8y9bhOC8xeK8xeK8xeK8xePMxeK8xeK8xeK8xePMxeK8xeK8xeK8xeK8zVuOT5jXr18/p/8E+wipoaDLYYWN3Y2PBuctFuctFuctFuctHmcuFuctFuctFuctHmcuFuctFuctFuctFudt3nL8pp+3bt2iI0eOpPvYkSNH6Pbt2zndBZYLKSQ1eRd/RgpJbeyufBQ4b7E4b7E4b7E4b/E4c7E4b7E4b7E4b/E4c7E4b7E4b7E4b7E4b/OW4xPmw4cPp507d6b72O+//04jRozI6S6wXEghERV3eEkK6cPLsuzjvMXivMXivMXivMXjzMXivMXivMXivMXjzMXivMXivMXivMXivM1bjk+Ynz17lvz9/dN9rH79+nT27Nmc7gJjjDHGGGOMMcYYY4wx9kE5PmEeGxtLNjY26T5mZWVFMTExOd0FxhhjjDHGGGOMMcYYY+yDcnzCvFixYnTmzJl0Hztz5gwVKVIkp7vAciE1JLr1pCCpwb9tEYHzFovzFovzFovzFo8zF4vzFovzFovzFo8zF4vzFovzFovzFovzNm85PmHevn17mjVrFh06dEin/fDhw/T9999TYGBgTneB5UJqSPRfRCEuPIJw3mJx3mJx3mJx3uJx5mJx3mJx3mJx3uJx5mJx3mJx3mJx3mJx3uYtxyfMJ02aRMWLF6cmTZqQp6cnNW3alDw9Palx48ZUvHhxmjJlSk53geVCSoWaapWJIKWC7zYsAuctFuctFuctFuctHmcuFuctFuctFuctHmcuFuctFuctFuctFudt3nJ8wrxAgQJ0+vRpmjJlChUqVIgePHhAhQoVoqlTp9KpU6fIzs4up7vAciGJiJxsXxF/TycG5y0W5y0W5y0W5y0eZy4W5y0W5y0W5y0eZy4W5y0W5y0W5y0W523eLET8ERsbG5o4cSJNnDhRxJ9jjDHGGGOMMcYYY4wxxjItx88wj4yMpFu3bqX72K1btygqKirTr7l06VJyd3enfPnyka+vLx07dizDZY8fP0516tQhBwcHsrKyovLly9OCBQsy/TcZY4wxxhhjjDHGGGOM5W45fob5l19+SQUKFKAff/wxzWPz5s2juLg42rRpk96vt3nzZho6dCgtXbqU6tSpQ8uXL6eAgAD6999/qXjx4mmWz58/P3311Vfk5eVF+fPnp+PHj9Pnn39O+fPnp4EDB2ZrbMx4UiDR5TAnSuGbJwjBeYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvFeZu3HD/D/MSJE9S8efN0H2vevDkdP348U683f/586tevH/Xv3588PT0pODiY3NzcKCQkJN3lq1SpQl27dqWKFStSyZIlqUePHtS8efP3npXOTB8gUVi0HYELjxCct1ict1ict1ict3icuVict1ict1ict3icuVict1ict1ict1ict3nL8TPMo6KiyMHBId3HChYsSJGRkXq/VnJyMp0/f57Gjh2r096sWTM6efKkXq9x8eJFOnnyJE2fPj3DZZKSkigpKUn+d1xcHBERqVQqUqlURESkUChIoVCQWq0mtfrtHW817SkpKQTgg+1KpZIkSZJfV7udiCglJUWvdgsLCwKg0y5JEimVyjR9zKj9fWMiSr3Dr/bbPAUSAVLadrVEIIks3rkTsEqdupSFAnq2K0gikFKrHUSUolaQhTKF/MuF04lbRSlFrZDbFRJIIb1dXg0iNRSkkNSk0OqkGhKp0+t7NsakvQ6zu54sFGqTGJM8HoWaapd9TEf/K0bSO7eseN96kiSQUkrbbqgxGer9lDYD441JqVCThUJNdTwe04lbRSlZpczx95PeY1KrDVL3JILpjAlEkkRUv9wjOnE7tZ6ktufc+0nfMWm2Y0PUcguF2iTGRJRaT+qUfUxH/nNN7U8Ov5/0HdO7tZko67Vc+/VNoZYrtWpKkkopt+suL77uaWpKdmt5ao9MY0wpkEghIU1NEbFv9KExGWofVjMGUxgTkW5NAUjYvtGHxmSo/XKR+3sfGpNEIEuLFLmeqNQKYftGHxqTSqUy2PFT6v8bf0xEqetDqVBT/fJvj3s07UTGq3va9Tm7x7maMRt7TJrmt8c9rkREwo9zMxpTZo7d31fLjXHs/r71lEf5tqa8SVEIP87NqF2lUhlmjkVSm8yYiFKz9y/3iE5q7aOIPM7NaEz6zKXoW8u151KMXcs1++BHbhZLnccSfJyb0Zg0OeamOUt95yPeHcf75PiEubOzM129epUaNmyY5rGrV69mOJmenqioKEpJSSFnZ+c0f+PJkyfvfa6rqytFRkaSSqWiKVOmUP/+/TNcdubMmTR16tQ07RcvXqT8+fMTEZGTkxOVLl2aQkNDdSb9XV1dydXVlW7dukWxsbFye6lSpahw4cJ07do1SkxMlNvLly9P9vb2dPHiRZ0Nx8vLiywtLencuXM6fahWrRolJyfTlStX5DalUknVq1en2NhYunnzptxuZWVF3t7eFBUVRffu3ZPbCxQoQJ6envT48WN69OiR3P6+MRERVS/1lJxsX8ntl8OcKCzajvzLhZNNvmS5/fSdIhT50pqaVn6gU9AP33CjxGQLCvAO1RnTvsvuZGWpogaeD+U2lVpB+y67k6NtItUqEyG3x7+2pEM33MitYDyVKhxLNvneEEAU+dKaTt8pQmVdYsjDJUZePizali6HFabKblFU3OGl3H7rSUH6L6KQQcd07tzbbTC76ynAO9EkxqQhSUQEkG2+N1SvXLj+66nQS/Iu/nZbMvSYDPV+0h6rscfkXy6cbK2SqbDdK7LJ94ZO3RbwftJzTKGhKQape4629iYzprBoW7r+yJGKFIyn5l4PSPP5mpPvJ33HpKkphqjlAd6JJjEmotR6Uih/Ikn/kZD3k75jSklJMdhnrnY2plDLJYnkmrL3Us6+nzIzpqgoa4PsG1koXExmTJfDnOhxjA0Vd4wjG6s3ck0Rsm/0gTFpakp292EDvBNNZkxEqTXFNl8SSURUSeC+0YfGZKj9cpH7ex8ak6NtIvmVjZDryctEcftGHxrTuXNPDHb8RFTQJMakWU9vVAqd4x591lNO1z3t457sHudqaoqxx6RZT5JElC+PiiQiqmeM49wMxpSZY/f31XKjHLu/Zz2VKxIj15QHUeKPczMa08WLkQaZY6nsZmEyY1KpFXTgagkqZPNa57hH5HFuRmPSrinZreUB3okmMSYLhVreB79wvzDFv7YUfpyb0ZiISuS6OUt95yMSEhJIXxK0p99zwMCBA2nnzp107Ngx8vDwkNtv375N/v7+1Lp163Svb56ex48fU7FixejkyZPk5+cnt8+YMYPWrVunE/y7QkNDKT4+nk6fPk1jx46lxYsXU9euXdNdNr0zzN3c3Cg6Oprs7OyI6OM7w7zJV4+MflYi0dtvz/IoUyjAK5T2Xy2ZehYPGf8Mvr3BrnJ7dtdTy6GPTGJMcr8Vampa+T7tu+xOuudZGPds7AOLihnk/dT86wcmMyalQk15FGpqVvk+7b9akpJM6AzzPxYWN0jda/5NuMmMSY3UsxEDvO/Rgf/Xk9R245+NrakphqjlLYc+MokxpT6eun3vvVyKUkzoDPO/lxQ32Gdui2/CTGJMRKnrw0Krprw2oTPMNTUlu/tGzb4JN5kxabbplt735H0UIuOfwaeQ3taU7O7Dthz6yGTGlPp6mn2UUnJN1zBm3ft7iZtB9subfHnfZMYkESivRYpcT96Y0Bnme4NdDXb81PTrcJMYE1Hq+rBQqKmld6hOTTH22dj7tI57snucq6kpxh6Tplm7poBM5wzzvxa7GuRMy6Zf3TeZMSkkkKXybU1JNqEzzPcGuxpkjqXFkEcmMyaNd497RB7nZjQmfeZS9K3l2nMpxq7lb4973EllQmeY/7WkRK6bs9R3PiIuLo4cHBwoNjZWnt/NSI6fYT5lyhTavXs3eXl5UcOGDcnV1ZUePXpEhw4dIkdHx3TP5M6Io6MjKZXKNGeTP3v2LM1Z5+9yd3cnIqLKlSvT06dPacqUKRlOmOfNm5fy5s2bpt3CwoIsLHQj06yAd2k2En3b333drLRLkpRue0Z9zGy75ic7+rarMmyX9G4HSem3Q/r/Ywqdv6MpaO9SQ0HqtJ8VBh1TetlndT2Zypje6TnR/zN/1/vWkyrdvhtmTIZ6P5nSmFLUCpLkv68g/P8jOKffT/qMSbN9ZrfumdKYiFJ/Kknp1BMi49Y9fT9v9GnX7pcp1HLNNiDi/ZR+H9O2Z1SbiTJfy9N7fWPXcsj1W9Jp1ya67smXfMt2LTedMaV2B+nuo6T20Xh1z1D7sKY0prek/7eL3jfKuN1Q++WmNCbtfRPN5ViITKOWa2dtiFpuCmPS6uV7aopx6l56mWW1lpvKmHRJGbRr+ii+7mW2dmS0PkxpTGro1hQ1NCeuGL/uabbP7NZyUxoTkeYSSOnXE2PWPX3nUjJq115P2uMyhVqO/8+jUAbvj9Tlxde93Dhnqc+YMupvenL8pp9Fixalc+fOUffu3enKlSu0Zs0aunLlCvXo0YPOnDlDRYsW1fu1LC0tydfXlw4cOKDTfuDAAapdu7berwNA5wxyZn5S1BKdvlOEUjIoOMywOG+xOG+xOG+xOG/xOHOxOG+xOG+xOG/xOHOxOG+xOG+xOG+xOG/zluNnmBOlTpqvXLky3cciIyPJyclJ79caPnw49ezZk6pVq0Z+fn60YsUKCgsLo0GDBhERUVBQEIWHh9PatWuJiGjJkiVUvHhxKl++PBERHT9+nObOnUtff/11NkfFjAkkUeRLa2N346PBeYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvFeZu3HD/DPD0AaO/evdShQwf5hpL66ty5MwUHB9O0adPIx8eHjh49Snv37qUSJUoQEVFERASFhb29dqharaagoCDy8fGhatWq0aJFi2jWrFk0bdo0g46JiWWhUFOAd2iaa3ixnMF5i8V5i8V5i8V5i8eZi8V5i8V5i8V5i8eZi8V5i8V5i8V5i8V5mzchZ5hr3L17l1atWkVr1qyhiIgIsrS0pA4dOmT6dQYPHkyDBw9O97HVq1fr/Pvrr7/ms8lzKS46YnHeYnHeYnHeYnHe4nHmYnHeYnHeYnHe4nHmYnHeYnHeYnHeYnHe5ivHJ8xfv35NW7dupZUrV9KxY8cIAEmSRMOHD6exY8eSg4NDTneBMcYYY4wxxhhjjDHGGPugHLsky9mzZ2nQoEHk4uJCvXv3pgsXLlDv3r1p9+7dBIDatGnDk+WMMcYYY4wxxhhjjDHGTEaOnGHu5eVF169fJyIiPz8/6tu3L3Xu3Jny589PsbGxOfEn2UdGpZbo8A03UvHdhoXgvMXivMXivMXivMXjzMXivMXivMXivMXjzMXivMXivMXivMXivM1bjkyYX7t2jSRJolatWtGsWbOoQoUKOfFn2EcuMVnoJfg/epy3WJy3WJy3WJy3eJy5WJy3WJy3WJy3eJy5WJy3WJy3WJy3WJy3+cqRS7IEBweTl5cX7d69mypXrkx+fn70008/0cuXL3Piz7GPkIUC/7/bMIzdlY8C5y0W5y0W5y0W5y0eZy4W5y0W5y0W5y0eZy4W5y0W5y0W5y0W523ecmTC/JtvvqGLFy/SmTNnaODAgXTz5k0aOHAgFSlShAYOHEiSJJEk8U8SGGOMMcYYY4wxxhhjjJmOHLvpJxFRtWrVKCQkhCIiImjNmjVUrVo12rZtGwGgfv360bx58yg6Ojonu8AYY4wxxhhjjDHGGGOM6SVHJ8w18uXLRz179qTDhw/TrVu3aOzYsfTq1SsaNWoUubm5iegCY4wxxhhjjDHGGGOMMfZeQibMtZUuXZq+++47CgsLo127dlGLFi1Ed4HlAiq1RPsuu/PdhgXhvMXivMXivMXivMXjzMXivMXivMXivMXjzMXivMXivMXivMXivM2b8Alz+Q8rFNS6dWv69ddfjdUFZuasLFXG7sJHhfMWi/MWi/MWi/MWjzMXi/MWi/MWi/MWjzMXi/MWi/MWi/MWi/M2X0abMGcsOywUoAaeD/luw4Jw3mJx3mJx3mJx3uJx5mJx3mJx3mJx3uJx5mJx3mJx3mJx3mJx3uaNJ8wZY4wxxhhjjDHGGGOMMeIJc8YYY4wxxhhjjDHGGGOMiHjCnJkxlZo3X5E4b7E4b7E4b7E4b/E4c7E4b7E4b7E4b/E4c7E4b7E4b7E4b7E4b/NlYewOMJYVKrWC9l12N3Y3Phqct1ict1ict1ict3icuVict1ict1ict3icuVict1ict1ict1ict3njrzqYWZII5GT7iiTimyeIwHmLxXmLxXmLxXmLx5mLxXmLxXmLxXmLx5mLxXmLxXmLxXmLxXmbN54wZ2ZJqQDVKhNBSr7bsBCct1ict1ict1ict3icuVict1ict1ict3icuVict1ict1ict1ict3njCXPGGGOMMcYYY4wxxhhjjHjCnDHGGGOMMcYYY4wxxhgjIp4wZ2YKRBT/2pKvBCUI5y0W5y0W5y0W5y0eZy4W5y0W5y0W5y0eZy4W5y0W5y0W5y0W523eLIzdAcayIkWtoEM33IzdjY8G5y0W5y0W5y0W5y0eZy4W5y0W5y0W5y0eZy4W5y0W5y0W5y0W523ezPIM86VLl5K7uzvly5ePfH196dixYxku++uvv1LTpk3JycmJ7OzsyM/Pj/7880+BvWU5QZJAxR3iSJL4uzoROG+xOG+xOG+xOG/xOHOxOG+xOG+xOG/xOHOxOG+xOG+xOG+xOG/zZnYT5ps3b6ahQ4fS+PHj6eLFi1SvXj0KCAigsLCwdJc/evQoNW3alPbu3Uvnz5+nhg0bUps2bejixYuCe84MSSmBvItHkpILjxCct1ict1ict1ict3icuVict1ict1ict3icuVict1ict1ict1ict3kzuwnz+fPnU79+/ah///7k6elJwcHB5ObmRiEhIekuHxwcTKNHj6bq1atT2bJl6bvvvqOyZcvS77//LrjnjDHGGGOMMcYYY4wxxkyZWV3DPDk5mc6fP09jx47VaW/WrBmdPHlSr9dQq9X08uVLKlSoUIbLJCUlUVJSkvzvuLg4IiJSqVSkUqmIiEihUJBCoSC1Wk1qtVpeVtOekpJCAD7YrlQqSZIk+XW124mIUlJS9Gq3sLAgADrtkiSRUqlM08eM2t83JiIipUJNktbfTIFEgJS2XS0RSCILhZq0qdSpS1kooGe7giQCKbXaQanXgZIkkESQ/4amXSGBFFrf3qlBpIaCFJKaFFqdVEMidXp9z8aYtNdhdteThUJtEmOS+61QU2rKSGf5968n7W9TDT0mQ72fTGlMSoWaLBRqefsW9X7Sa0xqtUHqnkQwnTHh7RLa/cnJ95O+Y9Jsx4ao5RYKtUmMSdMX6f+3vhHxftJ3TO/WZqKs13Lt1zeFWq5dU3L6/ZSZMWlqSnZrOZFm2zL+mFKQ+i/pnZpiCrXcUPuwmjGYwphSX0+zj0JC940+NCZD7Zeb0pg027Xm/0XuG31oTCqVymDHT6n/b/wxEWnWB9LUFGPXcu36nN3jXM24jD0mTbN2TTHWcW56Y8rMsfv7arkpjUkh6dYUYxznZtSuUqkMM8ciqU1mTG/p1hNTqOX6zKXoW8tN6fPp7XFP6v9EH+dmNCZNjrlpzlLf+Yh3x/E+ZjVhHhUVRSkpKeTs7KzT7uzsTE+ePNHrNebNm0cJCQnUqVOnDJeZOXMmTZ06NU37xYsXKX/+/ERE5OTkRKVLl6bQ0FCKjIyUl3F1dSVXV1e6desWxcbGyu2lSpWiwoUL07Vr1ygxMVFuL1++PNnb29PFixd1NhwvLy+ytLSkc+fO6fShWrVqlJycTFeuXJHblEolVa9enWJjY+nmzZtyu5WVFXl7e1NUVBTdu3dPbi9QoAB5enrS48eP6dGjR3L7+8ZERFS91FNysn0lt18Oc6KwaDvyLxdONvmS5fbTd4pQ5Etralr5gU4hPnzDjRKTLSjAO1RnTPsuu5OVpYoaeD6U21RqBe277E6OtolUq0yE3B7/2pIO3XCjYgXjyT5/MjXzekAAUeRLazp9pwiVdYkhD5cYefmwaFu6HFaYKrtFUXGHl3L7rScF6b+IQgYd07lzb7fB7K6nAO9EkxiThiQRRb20pvz53pB/uXC915NboZfkXfzttmToMRnq/aQ9VmOPyb9cONlaJZO9der2fep2zr+f9B1TaGiKQeqeo629yYwpLNqWrj1yJIWC5Hqi73rK6bqnqSmGqOUB3okmMSai1HpibakiEAl5P+k7ppSUFIN95mpnYwq1XJJIril7L+Xs+ykzY4qKsjbIvpGFwsVkxnQ5zInCY2zIOq9Kp6aI2Df60Jg0NSW7+7AB3okmMyai1JoiSakHjCL3jT40JkPtl5vSmBxtE8mvbIRcT14mits3+tCYzp17YrDjJ6KCJjEmzXpKVil0jnv0WU85Xfe0j3uye5yrqSnGHpNmPUkSUdIbCwKRUY5zMxpTZo7d31fLTWlMZV1iqFyRGLmmPIgSf5yb0ZguXow0yBxLZTcLkxmTSq2g/VdLUEKSpU49EXmcm9GYtGtKdmt5gHeiSYzJQqGW98Hz53tDCa8thR/nZjQmohK5bs5S3/mIhIQE0pcE7el3E/f48WMqVqwYnTx5kvz8/OT2GTNm0Lp163SCT8+mTZuof//+tHPnTmrSpEmGy6V3hrmbmxtFR0eTnZ0dEX18Z5g3+eqR0c9KJDKNbz8z6vveYFe5PbvrqeXQRyYxJv3ajbeeDiwqZpD3U/OvH5jMmEx5Pf2xsLhB6l7zb8JNZkymvJ40NcUQtbzl0EcmMSYNU1xPfy8pbrDP3BbfvL2vijlue+/2PafWk6amZHffqNk34SYzJlNeT5qakt192JZDH5nMmDRMcT39vcTNIPvlTb68bzJjMuX1tDfY1WDHT02/DjeJMRGZ7nrap3Xck93jXE1NMfaYTH09/bXY1SBnWjb96r7JjMmU19PeYFeDzLG0GPLIZMaU2m6a60mfuRR9a7n2XIo5bnup7Tm7nv5aUiLXzVnqOx8RFxdHDg4OFBsbK8/vZsSszjB3dHQkpVKZ5mzyZ8+epTnr/F2bN2+mfv360datW987WU5ElDdvXsqbN2+adgsLC7Kw0I1MswLepdlI9G1/93Wz0i5JUrrtGfUxs+0p6vQveZ9RuyrDdknvdpCUbrtERKWdX9DtJwXln5QQvS1o71JDQWqkaTbomNLLPqvrSfvvGHNMct8kULkiMXT7ScF0l89oPQESqdLtu2HGZKj3kymNSfNhV9YlNW/8/yM4J99P+o5Js31mt+6Z0piI6P95p60nRMate/p+3ujTrt0vY9dy7e1bLeD9lH4f07ZnVJuJMl/L03t9Y9Zy7cxJfv/lzPsptV2/McmXfMt2LTedMRGlbuNl0tlHSe2j8eqeofZh3x2DsWu5bk0Rt2/0oXZD7Zeb0phAEqlBOjWcSMy+Ufp9fNuunbUharkpjElDISHd457U5Y1T99LLLKu1PG1NMW4tV0ggj/9v48Y4zs1oTJmtHRmtD1Mak2YdvVtTTKHuabbP7NZyNRTy/xt7TERp98O1GbPu6TuXklG79nrSzsjYtTzNcY/g49zU9vTHlBvnLPUZU0b9TY9Z3fTT0tKSfH196cCBAzrtBw4coNq1a2f4vE2bNlHv3r1p48aN1KpVq5zuJhNAsyOj/Q0ayzmct1ict1ict1ict3icuVict1ict1ict3icuVict1ict1ict1ict3kzqzPMiYiGDx9OPXv2pGrVqpGfnx+tWLGCwsLCaNCgQUREFBQUROHh4bR27VoiSp0s/+yzz+iHH36gWrVqyWenW1lZUYECBYw2DsYYY4wxxhhjjDHGGGOmxewmzDt37kzR0dE0bdo0ioiIoEqVKtHevXupRIkSREQUERFBYWFvrx26fPlyUqlU9OWXX9KXX34pt/fq1YtWr14tuvuMMcYYY4wxxhhjjDHGTJTZTZgTEQ0ePJgGDx6c7mPvToIfPnw45zvEhFMj9a6/6V2LiRke5y0W5y0W5y0W5y0eZy4W5y0W5y0W5y0eZy4W5y0W5y0W5y0W523ezHLCnDE1FHQ5rLCxu/HR4LzF4rzF4rzF4rzF48zF4rzF4rzF4rzF48zF4rzF4rzF4rzF4rzNm1nd9JMxDYWkJu/iz0ghqY3dlY8C5y0W5y0W5y0W5y0eZy4W5y0W5y0W5y0eZy4W5y0W5y0W5y0W523eeMKcmSWFRFTc4SUpJGP35OPAeYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvFeZs3njBnjDHGGGOMMcYYY4wxxognzBljjDHGGGOMMcYYY4wxIuIJc2am1JDo1pOCpAb/tkUEzlsszlsszlsszls8zlwszlsszlsszls8zlwszlsszlsszlssztu8WRi7A4xlhRoS/RdRyNjd+Ghw3mJx3mJx3mJx3uJx5mJx3mJx3mJx3uJx5mJx3mJx3mJx3mJx3uaNzzBnZkmpUFOtMhGkVPDdhkXgvMXivMXivMXivMXjzMXivMXivMXivMXjzMXivMXivMXivMXivM0bT5gzsyQRkZPtK+IftojBeYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvFeZs3njBnjDHGGGOMMcYYY4wxxognzBljjDHGGGOMMcYYY4wxIuIJc2amUiDR5TAnSuG7DQvBeYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvHmYvFeYvFeYvFeYvFeZs3C2N3gLGsACQKi7Yzdjc+Gpy3WJy3WJy3WJy3eJy5WJy3WJy3WJy3eJy5WJy3WJy3WJy3WJy3eeMzzJlZUirU1NDzId9tWBDOWyzOWyzOWyzOWzzOXCzOWyzOWyzOWzzOXCzOWyzOWyzOWyzO27zxhDkzSxIR2eRL5rsNC8J5i8V5i8V5i8V5i8eZi8V5i8V5i8V5i8eZi8V5i8V5i8V5i8V5mzeeMGeMMcYYY4wxxhhjjDHGiCfMGWOMMcYYY4wxxhhjjDEi4glzZqZS1BKdvlOEUtT84xYROG+xOG+xOG+xOG/xOHOxOG+xOG+xOG/xOHOxOG+xOG+xOG+xOG/zZmHsDjCWFSCJIl9aG7sbHw3OWyzOWyzOWyzOWzzOXCzOWyzOWyzOWzzOXCzOWyzOWyzOWyzO27zxGebMLFko1BTgHUoWfLdhIThvsThvsThvsThv8ThzsThvsThvsThv8ThzsThvsThvsThvsThv88YT5sxscdERi/MWi/MWi/MWi/MWjzMXi/MWi/MWi/MWjzMXi/MWi/MWi/MWi/M2X2Y5Yb506VJyd3enfPnyka+vLx07dizDZSMiIqhbt25Urlw5UigUNHToUHEdZYwxxhhjjDHGGGOMMWY2zG7CfPPmzTR06FAaP348Xbx4kerVq0cBAQEUFhaW7vJJSUnk5ORE48ePJ29vb8G9ZYwxxhhjjDHGGGOMMWYuzG7CfP78+dSvXz/q378/eXp6UnBwMLm5uVFISEi6y5csWZJ++OEH+uyzz6hAgQKCe8tyikot0eEbbqTiuw0LwXmLxXmLxXmLxXmLx5mLxXmLxXmLxXmLx5mLxXmLxXmLxXmLxXmbN7OaME9OTqbz589Ts2bNdNqbNWtGJ0+eNFKvmLEkJlsYuwsfFc5bLM5bLM5bLM5bPM5cLM5bLM5bLM5bPM5cLM5bLM5bLM5bLM7bfJnVmouKiqKUlBRydnbWaXd2dqYnT54Y7O8kJSVRUlKS/O+4uDgiIlKpVKRSqYiISKFQkEKhILVaTWr124v4a9pTUlIIwAfblUolSZIkv652OxFRSkqKXu0WFhYEQKddkiRSKpVp+phR+/vGRESkVKhJ+3uxFEgESGnb1RKBpDQ3N9B8q2ahgJ7tCpIIpNRqBxGlqBWUR6mmAK9Q2n+1JKnUCrldIYEU0tvl1SBSQ0EKSU0KrU6qIZE6vb5nY0za6zC768lCoTaJMcn9VqipaeX7tO+yOxFJ7yyf8XqSJJBSSttuqDEZ6v2UNgPjjUmpUFMehZqaVb5P+6+WpCSVMsffT3qPSa02SN2TCKYzJhApJKIA73t04P/1JLU9595P+o5Jsx0bopZbKNQmMSZNX5pVvk97L5eiFFCOv5/0HdO7tZko67Vc+/VNoZZbaNWU1yql3K67vPi6p6kp2a3lqT0yjTGlQCKlBGrpfU/eRyESs2/0oTEZah9WMwZTGFPq62n2UUqRGhC2b/ShMRlqv1zk/t6HxiQRKK9FilxP3qgVwvaNPjQmlUplsOOn1P83/piIUteHhUJNLb1DdWpKTu8bfWhM2vU5u8e5mjEbe0yaZu2akrrH+pYx615mjt3fV8uNcez+vvVkqXxbU5JTFMKPczNqV6lUhpljkdQmMyaNd497RB7nZjQmfeZS9K3l2nMpxq7lb4973EmlVgg/zs1oTJocc9Ocpb7zEe+O433MasJcQ7MjowEgTVt2zJw5k6ZOnZqm/eLFi5Q/f34iInJycqLSpUtTaGgoRUZGysu4urqSq6sr3bp1i2JjY+X2UqVKUeHChenatWuUmJgot5cvX57s7e3p4sWLOhuOl5cXWVpa0rlz53T6UK1aNUpOTqYrV67IbUqlkqpXr06xsbF08+ZNud3Kyoq8vb0pKiqK7t27J7cXKFCAPD096fHjx/To0SO5/X1jIiKqXuopOdm+ktsvhzlRWLQd+ZcLJ5t8yXL76TtFKPKlNTWt/ECnoB++4UaJyRYU4B2qM6Z9l93JylJFDTwfym0qtYL2XXYnR9tEqlUmQm6Pf21Jh264kWvBeCpcIJGaeT0ggCjypTWdvlOEyrrEkIdLjLx8WLQtXQ4rTJXdoqi4w0u5/daTgvRfRCGDjuncubdf2mR3PQV4J5rEmDQkiYhAZJPvDdUrF673enIr9JK8i7/dlgw9JkO9n7THauwx+ZcLJ1urZCpsl7p9n7qd8+8nfccUGppikLrnaGtvMmMKi7al648cyc4qWa4n+q6nnK57mppiiFoe4J1oEmMiSq0nhfK/JiIS8n7Sd0wpKSkG+8zVzsYUarkkkVxT9l7K2fdTZsYUFWVtkH0jC4WLyYzpcpgTPY6xoUI2r3Vqioh9ow+NSVNTsrsPG+CdaDJjIkqtKbb/f0+I3Df60JgMtV9uSmNytE0kv7IRcj15mShu3+hDYzp37onBjp+ICprEmDTr6Y1KoXPco896yum6p33ck93jXE1NMfaYNOtJkojy5UntrzGOczMaU2aO3d9Xy01pTGVdYqhckRi5pjyIEn+cm9GYLl6MNMgcS2U3C5MZk0qtoANXS5ClhVqnnog8zs1oTNo1Jbu1PMA70STGZKFQy/vgNvneUPxrS+HHuRmNiahErpuz1Hc+IiEhgfQlQXv63cQlJyeTtbU1bd26lQIDA+X2IUOG0KVLl+jIkSPvfX6DBg3Ix8eHgoOD37tcemeYu7m5UXR0NNnZ2RHRx3eGeZOvHhn9rEQi7TPMU0zuDPO9wa5ye3bXU8uhj0xiTHK/TfQM8wOLihnk/dT86wcmMyZTPsP8j4XFDVL3mn8TbjJjMuUzzDU1xRC1vOXQRyYxptTHtc8wl0zmDL6/lxQ32Gdui2/e3ojcFGq5qZ5hrqkp2d03avZNuMmMyZTPMNfUlOzuw7Yc+shkxpT6etpnmJNJnMGnUivo7yVuBtkvb/LlfZMZkymfYb432NVgx09Nvw43iTERme4Z5vu0jnuye5yrqSnGHlP6Z5hTjr6fMjOmvxa7GuRMy6Zf3TeZMZnyGeZ7g10NMsfSYsgjkxmThimeYa7PXIq+tVx7LsXYtdxUzzD/a0mJXDdnqe98RFxcHDk4OFBsbKw8v5sRszrD3NLSknx9fenAgQM6E+YHDhygdu3aGezv5M2bl/LmzZum3cLCgiwsdCPTrIB3aTYSfdvffd2stEuSlG57Rn3MbHuKOv1L3mfUrsqwPf1fA6TXDpLSb4f0/8cUOn9HU9DepYaC1Gk/Kww6pvSyz+p6MpUxvdNzov9n/q73rSdVun03zJgM9X4ypTGlqBUkyX9fIf8QNKffT/qMSbN9ZrfumdKYiFJ/Kknp1BMi49Y9fT9v9GnX7pcp1HLNNiDi/ZR+H9O2Z1SbiTJfy9N7fWPXcsj1W9Jp1ya67smXfMt2LTedMaV2B+nuo6T20Xh1z1D7sKY0prek/7eL3jfKuN1Q++WmNCbtfRPV/yfLiUyjlmtnbYhabgpj0urle2qKcepeeplltZabyph0SRm0a/oovu5ltnZktD5MaUxq6NYUNTQnrhi/7mm2z+zWclMaE5HmEkjp1xNj1j1951IyatdeT9rjMoVajv/Po1AG74/U5cXXvdw4Z6nPmDLqb3rMasKciGj48OHUs2dPqlatGvn5+dGKFSsoLCyMBg0aREREQUFBFB4eTmvXrpWfc+nSJSIiio+Pp8jISLp06RJZWlpShQoVjDEEZgAqtUT7LrtnWHCYYXHeYnHeYnHeYnHe4nHmYnHeYnHeYnHe4nHmYnHeYnHeYnHeYnHe5s3sJsw7d+5M0dHRNG3aNIqIiKBKlSrR3r17qUSJEkREFBERQWFhYTrPqVKlivzf58+fp40bN1KJEiXo/v37IrvODMzKUkUvX+cxdjc+Gpy3WJy3WJy3WJy3eJy5WJy3WJy3WJy3eJy5WJy3WJy3WJy3WJy3+Ur/9wMmbvDgwXT//n1KSkqi8+fPk7+/v/zY6tWr6fDhwzrLA0jzP54sN28WClADz4fpXpOLGR7nLRbnLRbnLRbnLR5nLhbnLRbnLRbnLR5nLhbnLRbnLRbnLRbnbd7McsKcMcYYY4wxxhhjjDHGGDM0njBnjDHGGGOMMcYYY4wxxognzJkZy+gO0SxncN5icd5icd5icd7iceZicd5icd5icd7iceZicd5icd5icd5icd7my+xu+skYUWrR2XfZ3djd+Ghw3mJx3mJx3mJx3uJx5mJx3mJx3mJx3uJx5mJx3mJx3mJx3mJx3uaNv+pgZkkikJPtK5KIb54gAuctFuctFuctFuctHmcuFuctFuctFuctHmcuFuctFuctFuctFudt3njCnJklpQJUq0wEKfluw0Jw3mJx3mJx3mJx3uJx5mJx3mJx3mJx3uJx5mJx3mJx3mJx3mJx3uaNJ8wZY4wxxhhjjDHGGGOMMeIJc8YYY4wxxhhjjDHGGGOMiHjCnJkpEFH8a0u+EpQgnLdYnLdYnLdYnLd4nLlYnLdYnLdYnLd4nLlYnLdYnLdYnLdYnLd5szB2BxjLihS1gg7dcDN2Nz4anLdYnLdYnLdYnLd4nLlYnLdYnLdYnLd4nLlYnLdYnLdYnLdYnLd54zPMmVmSJFBxhziSJP6uTgTOWyzOWyzOWyzOWzzOXCzOWyzOWyzOWzzOXCzOWyzOWyzOWyzO27zxhDkzS0oJ5F08kpRceITgvMXivMXivMXivMXjzMXivMXivMXivMXjzMXivMXivMXivMXivM0bT5gzxhhjjDHGGGOMMcYYY8QT5owxxhhjjDHGGGOMMcYYEfGEOTNTIKLIl9Z8t2FBOG+xOG+xOG+xOG/xOHOxOG+xOG+xOG/xOHOxOG+xOG+xOG+xOG/zZmHsDjCWFSlqBZ2+U8TY3fhocN5icd5icd5icd7iceZicd5icd5icd7iceZicd5icd5icd5icd7mjc8wZ2ZJIYHKFXlOCr55ghCct1ict1ict1ict3icuVict1ict1ict3icuVict1ict1ict1ict3njCXNmlhQSyMMlhguPIJy3WJy3WJy3WJy3eJy5WJy3WJy3WJy3eJy5WJy3WJy3WJy3WJy3eeMJc8YYY4wxxhhjjDHGGGOMeMKcMcYYY4wxxhhjjDHGGCMinjBnZkoNorBoW1LzL1uE4LzF4rzF4rzF4rzF48zF4rzF4rzF4rzF48zF4rzF4rzF4rzF4rzNm1lOmC9dupTc3d0pX7585OvrS8eOHXvv8keOHCFfX1/Kly8flSpVipYtWyaopyynqKGgy2GFSQ2z3ITNDuctFuctFuctFuctHmcuFuctFuctFuctHmcuFuctFuctFuctFudt3sxurW3evJmGDh1K48ePp4sXL1K9evUoICCAwsLC0l0+NDSUWrZsSfXq1aOLFy/SuHHj6JtvvqHt27cL7jkzJIWkJu/iz0ghqY3dlY8C5y0W5y0W5y0W5y0eZy4W5y0W5y0W5y0eZy4W5y0W5y0W5y0W523ezG7CfP78+dSvXz/q378/eXp6UnBwMLm5uVFISEi6yy9btoyKFy9OwcHB5OnpSf3796e+ffvS3LlzBfecGZJCIiru8JIUkrF78nHgvMXivMXivMXivMXjzMXivMXivMXivMXjzMXivMXivMXivMXivM2bWU2YJycn0/nz56lZs2Y67c2aNaOTJ0+m+5xTp06lWb558+Z07tw5evPmTY71lTHGGGOMMcYYY4wxxph5sTB2BzIjKiqKUlJSyNnZWafd2dmZnjx5ku5znjx5ku7yKpWKoqKiqEiRImmek5SURElJSfK/Y2NjiYjo+fPnpFKpiIhIoVCQQqEgtVpNavXbn1do2lNSUgjAB9uVSiVJkiS/rnY7EVFKSope7RYWFgRAp12SJFIqlWn6mFH7+8akSn5JSoWatL8YS4FEgJS2XS0RSCILhe7PTlTq1KUsFNCzXUESgZRa7SCiFLWCJGUKJSXGE1SxRGqF3K6QQArp7fJqpF43SiGpdb7VU0MidXp9z8aYnj9/Lrdnez2pYk1iTBpQqOl1YjypkuOISHpn+fesJwmklNK2G2pMMTExBnk/kSrWZMakVKgJCrW8faeolDn/ftJzTC9evDBI3UtJjjOZMalBpJKIXmvVk9T2nHs/6TsmTU0xSC1XxZrEmIhI3r5VyS8pBVKOv5/0HVNsbKzBPnO1a4op1HLtmqJSKeV23eXF1z1NTcnuvlHqZ5NpjCkFEpEEnX0UIkH7Rh8Yk6amZHsf9v/btymMiUh7H+UlqUHC9o0+NKZ3a0qWa/mbWJMZk0QgpcXbfXCoFcL2jT40pufPnxvs+EmV/NIkxkT0//WhVcM1NSWn940+NCbt455sH+f+v6YYe0yaZu2aAqKcfT9lYkwvXrzQ+9j9fbUcqliTGZNCAkHruF6dohB+nJtR+/Pnzw0yx6J+E2syYyIiUlHa4x6Rx7kZjUmfuRS9a7nWXIqxa/nb4544UqkVwo9zMxpTXFxcrpuz1Hc+Ii4u9bhBe7mMSNBnKRPx+PFjKlasGJ08eZL8/Pzk9hkzZtC6devo5s2baZ7j4eFBffr0oaCgILntxIkTVLduXYqIiCAXF5c0z5kyZQpNnTo1ZwbBGGOMMcYYY4wxxhhjTLiHDx+Sq6vre5cxqzPMHR0dSalUpjmb/NmzZ2nOItdwcXFJd3kLCwtycHBI9zlBQUE0fPhw+d9qdeo3Xg4ODiRJfPEhUxAXF0dubm708OFDsrOzM3Z3cj3OWyzOWyzOWyzOWzzOXCzOWyzOWyzOWzzOXCzOWyzOWyzOWyzO2/QAoJcvX1LRokU/uKxZTZhbWlqSr68vHThwgAIDA+X2AwcOULt27dJ9jp+fH/3+++86bfv376dq1apRnjx50n1O3rx5KW/evDpt9vb22es8yxF2dnZceATivMXivMXivMXivMXjzMXivMXivMXivMXjzMXivMXivMXivMXivE1LgQIF9FrOrG76SUQ0fPhw+umnn2jVqlV048YNGjZsGIWFhdGgQYOIKPXs8M8++0xeftCgQfTgwQMaPnw43bhxg1atWkUrV66kkSNHGmsIjDHGGGOMMcYYY4wxxkyQWZ1hTkTUuXNnio6OpmnTplFERARVqlSJ9u7dSyVKlCAiooiICAoLC5OXd3d3p71799KwYcNoyZIlVLRoUVq4cCF16NDBWENgjDHGGGOMMcYYY4wxZoLMbsKciGjw4ME0ePDgdB9bvXp1mrb69evThQsXcrhXTKS8efPS5MmT01w6h+UMzlsszlsszlsszls8zlwszlsszlsszls8zlwszlsszlsszlssztu8SQBg7E4wxhhjjDHGGGOMMcYYY8ZmdtcwZ4wxxhhjjDHGGGOMMcZyAk+YM8YYY4wxxhhjjDHGGGPEE+aMMcYYY4wxxhhjjDHGGBHxhDljjDHGGGOMMcYYY4zphW8HmfvxhDljjDHGGGOMfcQSEhKM3YWPEk+4iHH37l1jd4GxHPPq1Stjd+Gjsn37diIikiSJa3guxxPmjDHGGGOMMZOiVquN3YWPxhdffEGLFy+mmJgYY3flozFv3jy6efMmT7gIMHXqVOrRowedP3/e2F35aLx588bYXfhofPLJJzR16lSKjY01dlc+Cps2baIBAwbQd999R0Q8aZ7b8YQ5M0lcdBhjWcWTLMbF9TtnZbR9p6SkCO7Jx4O3afE+/fRTGjp0KG/XgsTHx9OPP/5IGzdu5ElzAQ4dOkSrVq2iKVOm0J07d3jCJYeVKVOG7OzsaOrUqXT27FljdyfX69q1K/Xs2ZNev35t7K58FGrVqkVz586lJUuW0IsXL4zdnVzPz8+PvvrqK1q/fj19++23RMST5rkZT5gzk/DuBIAkSUTEB6k5JaMJF847Z6SXK2edM9RqNSkUqR9tP/30E40fP546d+5MR48epcjISCP3LnfKqH4zw9Pevnfs2EGLFy+mOXPm0OPHj0mpVBq5d7mTWq2Wt+nk5GR6+fKlkXv0cWjWrBktW7aMpkyZQiqVytjdybU0+yLr1q2jVq1aUXBwMG3YsIGeP39u5J7lbg0bNqTRo0dTREQEjRs3jm7fvs0TLjmoe/fu9NVXX5FSqaRp06bRpUuXjN2lXK1z5860e/duGj58OE+a57CUlBQaPXo0LV68mCZMmEDLli2j6OhoY3cr11KpVFSyZEkaMmQIde/enTZt2kTBwcFExJPmuZWFsTvAmPYEwM6dO+nu3buUJ08eqlevHvn4+BAAnoAxIO28d+/eTbGxsfTixQsaMGAAWVpaGrl3uY923o8fP6aEhAQqW7as/Dhv34alyXr06NG0bt066tKlCxERderUifr370/jx48nKysrY3YxV9Hevrdu3Uo3b94kCwsL8vPzowYNGhi3c7mQ9va9fft2cnd3pzx58tCYMWPo77//poYNGxq5h7mL9vY9c+ZMOnPmDJ05c4b69etHtWvXphYtWhi5h7lTSkoKDRgwgPLnz0+9evUipVJJQUFBlDdvXmN3LdfR3gcJCgqif//9l5YsWUJERD179qQCBQoYs3u5UnJyMllaWlKvXr0oPj6efv31V5o4cSLNmjWLSpYsyfuFBqRdw+3s7MjR0ZF+++03GjduHM2cOZO8vb2N3MPcJyUlhdq3b0/bt2+nTz75hJRKJc2cOZNsbGyM3bVcR61WyydLdO/enc6cOUPTp08npVJJAwYMIHt7e+N2MJcBQBYWqdOne/fupYcPH9KzZ89o0qRJ9ObNGxo1apQ8ac41PPfgM8yZ0Wl2ZEaNGkVDhw6lP/74g44ePUpVq1al/fv3c8ExMO0Jl6+//pp+/PFHWrZsGVWoUIGOHz/O34waEAA578mTJ1Pr1q3J39+f6tSpQ4sWLaIXL17w9p0D9u3bR1u3bqV9+/bRggULaMiQIfTs2TPy9vbmyXID064nI0eOpAsXLtDNmzepUaNGtGnTJiP3LnfauHEjrV27lrZs2UJ//fUXff7550REOmeEch03DM32PX78eFqwYAG1a9eO5syZQ9u2baMZM2ZQeHi4kXuY+6SkpMgTAFWrVqWvv/6apk2bRvPnz+czzXOAZhsfMmQIde7cmZRKJSUnJ1NQUBCtW7eOf95vYADkk1Pmz59PZ8+epfv379PWrVtp7NixdPfuXT5L0YA02/ewYcNo4MCBlD9/fmrYsCFduHCBxo8fz9c0NzDt+u3i4kIjRoygJUuW0LRp0ygxMdHIvct9tLfvOnXqEAAqW7YsjR07li/PkgM0x+wTJkyg4cOHU/Xq1WnmzJlUr149WrFiBc2YMUNejmt4LgLGTMAvv/yCIkWK4J9//gEArFu3DpIkYe3atUbuWe60cuVKFC5cGJcuXQIA7Ny5E5Ik4Y8//pCXUavVxuperjNjxgw4Oztj165dePXqFerUqYOyZcviypUrxu5arrRp0ya0aNECALBhwwbY2tpi6dKlAID4+HhcvXoVKSkpxuxirvLbb7+hWLFiOH36NIDU/CVJwqpVq4zcs9xp5syZGDZsGABg69atsLGxwfLlywEAsbGxiI2NNWb3cp3r16/Dy8sLR48eBQAcP34clpaWWL16NQBApVIZs3u51qhRo1CuXDn069cPVapUgSRJmDBhAt68eWPsruU6W7duhb29PS5duoSXL18CAPr27QtnZ2csWrQIL168MHIPc5/Zs2fD1tYWe/bswaVLlzB58mT4+vqiU6dOuHv3LgDeDzeUU6dOoUiRIjh27Jjctn79evj7+6Nly5a8L54DRo0ahdKlS+Orr75C3bp1YWFhgS+++AKJiYnG7lqu8/vvv8POzg5nz56VPx9nz54NSZIwY8YMPH/+3Mg9zF3Cw8NRtWpVnTmq0NBQjBo1Cm5ubpg/f74Re8dyAk+YM5Pw3XffYdCgQQCA7du3w8bGBitWrACQOgHw4MEDY3Yv15k0aRKCgoIApH5ZYWdnh5CQEACpE4oavLOePWq1GjExMahfvz42bdoEAPjrr790JrjevHnDEy7ZkN7E9/fff4+6devi6NGjsLOzw5IlS+THNmzYgCFDhvAOpAEtWLAAPXr0AJB+/b5x44Yxu2fW0tu+v/zyS3z22Wf4/fffdb4MAoCQkBCMHj0aSUlJIruZq/3777+oVKkSgLdfUGg+LxMSErBjxw48efLEmF3Mdf744w/Y2tri5MmTAIC4uDiEhIRAqVRi4sSJSE5ONnIPc5cff/wRFSpUwPPnz3VqTvfu3WFvb48lS5YgMjLSiD3MPdRqNV6/fo0WLVpgzJgxOo8tW7YM7u7u6NKlC0JDQ+XlWfacPn0aBQoUkE/K0li5ciXy5s2L1q1by7WGZd+hQ4dgZ2cnf8mcmJiILVu2IF++fBg8eDASEhKM3MPcZcuWLShXrhyio6N16sW0adOQN29eLFiwgOu3Ab148QKurq6YNWuWTvuDBw9QsWJFFChQAOPGjTNS71hO4EuyMOHSu+FkfHw8qdVq+vXXX6lXr140Z84cGjBgABER/f777xQSEkLx8fGiu5orIJ2fBF28eJESEhLo2LFjNGDAAJo1axYNGjSIANC8efNozpw5RMQ378sK7bwlSSKVSkWRkZHUokUL+uOPP6h9+/Y0Z84cGjhwICUmJtLatWspNDTUiD02X9rXpvzzzz/ln9b26NGDnjx5QvXr16f58+fT4MGDiYjo9evXtHHjRoqLi+Pr+mVRevX7zZs3JEkSbdmyJU39/vPPP2nFihX8s9As0N6+L168KN+0tn79+nT58mXq1KkTzZgxg7744gsiIoqLi6M9e/aQWq3m+1FkUXrb98uXLykqKop++OEHnc9LIqJLly7R2rVrKSIiQnRXc7W4uDgqUqQIeXl5ERGRra0tDRo0iL7//nuaPn06LVy4kJKTk43cS/On2d5VKhXFxMRQnjx5SKFQ0KtXr4go9ZrmSUlJNGnSJDpy5Igxu5prSJJEefPmJWtr6zSXdPr888+pfv36tHv3burfvz+FhYXxfngmae+Da/47b9685OTkRPfv3yeit9t93759ycPDg65fv047duwQ3dVcKz4+ngoWLEiVKlUiIqJ8+fLRp59+SkuXLqWQkBD67rvv+Jg+izTbtPZ2bmFhQffu3ZMv85mUlERERO3btyeFQkHDhw+nPXv2GKW/5i69fUKlUkm1a9em69ev69Tw4sWLU82aNal8+fL0+PFjviRLLsIT5kwo7QmAs2fPyh+Y5cuXp/3799Nnn31G3333nXwwGhcXRxs2bKCUlBS+WUgWqNVqeWf7+vXr8oRLjx496PDhw9S4cWOaN2+ePOGSkJBAZ8+e5btrZ5F23rGxsURE5OjoSHny5KEuXbpQ586dacGCBfL2/eTJE1q7di1dvXrVaH02V9C6PvzYsWNpxIgRdO7cOXrx4gUVLFiQhgwZQqVLl6aDBw/Sv//+S3v27KHAwEAKCwujFStW8PXlskC7fh8/fpxiYmKIiKhs2bJ0+PBh6t27N82YMUPevuPj4+nnn3+mlJQU/oIik7SznjBhAvXu3ZvOnj1LAKhVq1ZUpkwZKly4MOXJk4ceP35Mly5doi5dulB4eDjNnDmTiPg65pmlnfmqVasoJCSEiIhq1KhBLVu2pGHDhtHw4cPpyy+/JCKixMRE+u677+jNmzfyxC7LvPS2U0dHR7pz5w79+++/RPT2oLVhw4aUL18+GjVqFK1evVpkN3OFdw/+Nfsrffr0ISsrK/r000+JiMja2pqIiJKSkqhHjx40bNgwat++vdC+5hbpTbgQpR73HD9+PM3+n4eHB3l7e1PNmjXJ1dVVRBdzDe198ISEBHkfxcfHh+rUqUNDhgyhM2fOyHX+yZMnVL58eZo8ebL8uckyJ7367erqSo8ePaJ//vlHZ5maNWuSvb09fffdd/LnK9Of9vatUqnkXAMDA6levXrUtWtXCg8Pl2+ObW1tTd988w2tW7eOunfvbrR+myvtfcKbN2/S+fPnKTo6mmxsbGjAgAG0Y8cOWrJkiXzSW0JCAsXGxtKAAQNo1apVfJyZmwg/p519tLR/5jlhwgR4eHhg165dclv37t2RN29erFmzBv/++y8uX76M5s2bo2rVqvI1ufinifrTznv8+PHw9/fHnj17oFarcfv2bbRs2RKVK1fGunXr8ObNG1y7dg0tW7aEr68vXyM0C7TzXrBgAYYPHy5fimLjxo1wdXVF8+bN5WXi4+PRsmVLNGrUiC/Jkg3Tpk2Dk5MTjh49qvMz/aSkJGzYsAHly5dHwYIFUaVKFbRv315ehjPPHO3aO378eJQqVQrbtm2Tt/shQ4ZAkiQsXrwYZ86cwdmzZ9GsWTP4+Phw/c6GCRMmwMXFBfv27dO5jFB8fDx69OiBihUrwsLCAjVq1EDDhg15+84i7W1z5MiRcHNzQ3BwsHw5uMuXLyMgIAA2NjaYPXs2Jk2ahCZNmqBixYpy5nxfhMzTzkxzGSFNvWjdujWaNGmCCxcuyMvcv38fgwcPxu7du3k/JZO0s16+fDl69+6Nzp07Y86cOQCAAwcOwM3NDfXr18fRo0dx9OhRtGjRAr169ZKfx3Ulc7Qz//PPP/Hrr79i/fr1cludOnVQoUIFnDhxApGRkXj9+jXat2+PhQsXyjWJ64p+tGv4zJkz0aBBA5QuXRrt27fHuXPnkJKSglatWsHZ2RlBQUFYuHAhGjZsiAYNGsgZc9aZo51XQkICVCoVXr9+DQDo2rUr6tati8OHD8vLPH78GIMGDcKhQ4e4fmeSdtaLFi1C586d0a5dO4wYMQIAcPLkSTRs2BDly5fH77//jt27d6N58+by/ZwAcOaZ8O4xT7ly5VCyZEmUKlUKY8aMQXJyMjZt2gRnZ2fUr18frVu3RvXq1VG5cmX5c5KPeXIPnjBnwo0fPx7Ozs7Yv38/nj17pvNYz549UaFCBSiVStSqVQsNGjTgCYBsGjduHJydnfH777/rTLhcv34dnTp1QrFixVCoUCH4+PjA39+f886mkSNHonDhwti0aRPu378PAIiIiMCUKVPg4OCABg0a4NNPP0W9evXg5eXFeWdDeHg4atSogW3btsn/Pnz4MAYPHoxly5bJy12+fBlPnz6Vd154pzHrJk6cCGdnZxw8eBDR0dE6jw0bNgyVK1eW63eTJk14+86G//77D+XLl8fu3bsBADExMbhx4wYWLlwoX4s1IiICe/fuxY0bN+QDKt6+s27RokUoXLgwzpw5k+axqKgojB07FtWqVUNAQAC++eYbOWvOPPO0JwB++OEHdO3aFc2bN8f48eMRGxuLkydPokWLFqhSpQrWr1+PPXv2oHnz5mjcuDHX8mwYPXo0ihYtipEjR2LOnDmQJAmjRo3Cy5cv8c8//6BWrVpwcXGBm5sb/Pz8+HrxBjBmzBgUL14c9erVQ+HChVG7dm2cOXMGCQkJqFu3Ltzd3eHu7o6KFSuibNmy/CVzNkycOBEuLi5YuXIlrl69CkdHR9StW1c+3hw1ahQaN24MLy8vtG3blr/wzCLtvObOnYsOHTqgdu3aGDp0KB49eoRr166hXbt2qFixIn744Qds3boVzZo1g7+/P9fvbBgzZgwKFy6MWbNmYeHChbC2tka7du2gUqlw8uRJdO3aFXZ2dihbtizq1avH9Tub5syZA2dnZ/z1118AgC5dusDR0RGnT58GABw9ehTff/89unXrhpEjR/IxTy7FE+ZMqLt376JSpUr4/fffAQDPnz/HjRs3MGfOHJw9exZA6hlEf/31F08AGMD58+dRqlQpHDlyBEDqjbNu3bqF9evX4+bNmwBSJ2V++eUXnD17lvPOpg0bNsDV1RXnz5+X25KSkuQbwh05cgRdunTBV199he+//54nW7IpISEBtWrVwrBhw/D333+jU6dOqFatGho0aABJkjB58mQAugedfFCUdQ8fPkSVKlWwfft2AEBkZCSuXLmCSZMm4e+//wYAPHnyBKdOncK9e/e4nmTT9evX4eHhgb/++gsHDx7EoEGD4OXlBVdXV1SqVAmbN29O8xzevrPuzZs36N69O8aPHw8AuHnzJtatW4e6deuiadOmuHz5MoDUGz69+zyWdWPGjIGTkxN++OEHzJw5E+7u7mjYsCGA1LOeP//8c+TNmxeenp6oW7eufEDKk4mZd+LECZQuXVq+Gd8ff/wBS0tL+SbNGleuXOF9cANZsWIFXFxccPHiRQDA2rVrIUkS9u/fLy+zbds2LFq0CAsXLpSz5gmXzAsNDYWPjw/27dsHADh27Bisra3TbN+vXr1CbGwsT9wawNixY+Ho6Igff/wRixcvhqenJ7y9vQGk1psxY8agQIEC8PLy0jkJjut35l28eBEVKlTAsWPHAAA7d+6Era0tFi1apLPc7du3ER4ezvU7G9RqNZKTk9GmTRssXboUALB7927Y2dnJJ2RpfhX3Ls479+EJcybUtWvX4OTkhGPHjuHw4cMYNGgQvL29UbhwYVSqVEnnEi0aPAGQdZcvX4aXlxcOHz6Mf/75B1999RXKlSsHd3d3lCxZUmeHXYPzzrrvvvsOjRs3BpA62RIcHAxPT084Ozvju+++S/c5fFCkn/S2y/j4eIwfPx41atSAhYUFRowYIZ8F0K9fP3zxxReiu5mr3b59Gw4ODti1axf+/vtv9OvXD1WrVoWrqyvKly+PjRs3pnkO1xP9ZHTw6OvrCw8PD1hYWODrr7/G7t278fTpU3h7e6c5SGLZ980336BMmTL44YcfULduXQQEBGDo0KGoU6cOKlWqhJSUFJ11xQf92XPx4kVUrFgRJ06cAADs2rULtra2CAkJ0VnuwYMHePz4MU8AZNNvv/2GOnXqAAB+/fVX2NjYyAf/L168wIEDB9I8h/dRsmfkyJEYM2YMAGDTpk0oUKCAPAETFxeX7nM486y5ceMGypcvDyB1MtHGxkauJS9fvsSGDRvkS4Zo8D5K1l2/fh3e3t7yBO7u3bvTrd/Pnj1DVFQUf0GRTX/88QfKlSsHANixY4dO/Y6NjeV98Gx6d38uLi4OPj4+uHnzJg4fPqyT9+vXr7F48WKcO3fOGF1lgvFNP1mOQTo3OqhYsSJ5eXnRp59+SgEBAWRpaUkzZsygR48ekUqlov/++y/NczQ3XGDvl17e9vb2pFaraezYsVS3bl1KSUmhWbNm0d69e8ne3p4eP36c5jmcd+Zpburk4uJCjx49os6dO9Mnn3xC//zzD3Xp0oWCgoJowoQJ9O+//6a5w7lSqTRav82F9o1Xzp8/T6dPn6YbN25Q/vz5ady4cbRt2za6fPkyzZ07lxo3bkxERLdu3SIHBwdjdtuspVdPypQpQ02bNqU+ffpQ69atyc7OjmbMmEEPHz4kGxsbrt9ZpH0jp7t371JYWBiFhYURUerNsWfNmkXHjh2jhQsXUqtWrahw4cJ8E+xsyuhGfB07dqQ6derQ999/Ty1btqQZM2bQggUL6JtvvqEiRYpQYmKivK6ISOe/WeZFR0dTUlIS1a5dm3bs2EHdunWj2bNn06BBgyg+Pp5++eUXSkxMpOLFi1ORIkVIoVCQWq0mCwsLY3fd5Glv4ykpKUREZGNjQ5Ik0ZIlS6hXr140Z84c+vzzz4mI6Ny5c7R06VK6f/++zuvwPor+3q0rKpWKLly4QPb29nT+/HkaMGAAzZo1i7744gtSq9W0ePFiWrNmTZrX4cw/TDvrhIQEIiIqVKgQqVQq+vrrr6lnz540d+5c+SbkoaGhtHz5cjp37pzO6/A+Sta9ePGCYmJiqG7durRz507q0qWLTv1eu3YtvXjxgpycnMjBwYEkSeL6rSft7VulUhERkZOTE5UoUYIWLlxIPXr0oLlz58r1+/r167Rr1y66ceOGzuvw9q0f7f1wzWegra0tubq6UocOHah169a0ePFiOe+YmBjaunUrXb582VhdZgJxxWI5QnuC6969ewSA8ufPTy4uLvTXX3/R9u3bydXVlWrWrCk/x9nZmfLly2esLps17bwfPnxIVlZWpFAoqHjx4rR9+3a6dOkSOTk5Ub169cjCwoIAyP/PMk87b21NmzalmJgYOnjwIA0bNowaNmxIpUuXpjNnzlDNmjXJzs5O/kDmiRb9AJCznjBhAq1fv54sLS3pwYMHNHPmTOrVqxe5ubkREVF8fDzdvn2bgoKCKDY2liZPnmzMrpst7e37v//+o+TkZLK3tyc3NzfatGkT/fnnn+Tk5ERVq1aVn2Nra8uTuFmgnfXUqVNp165d9PTpU/L09KRevXpRjx49KDAwkIhSJwWio6Plg1HNRADLHO3Mf/31V3r69Cm9efOG+vXrR/Xq1aN69epRVFQUOTo6ys/56aefyMHBgfLnz2+sbps97dwBkCRJZG1tTSVKlKCffvqJhg8frjMBcOnSJfrzzz/J29ubPD095dfhCYAP08568+bNZG1tTfXr16fixYtTcnIyDRs2jCZMmCDXkNevX9OCBQvI3t6eSpQoYcyumy3tzM+ePUslS5YkJycn6tGjB02aNIkmTJhAq1atos8++4yIiF69ekVHjhyh6tWrG7PbZkk768WLF9Pjx4+pf//+VKpUKQoMDKTly5dThw4d5Fry+vVrGjduHNnY2JCfn58xu262tDPX/Le1tTW5u7vTkiVLKCgoSKd+X7t2jQ4cOEA+Pj5kb28vvw7X7w/Tzvrnn38mW1tbatiwIRUpUoTCw8Np6NChNH36dDnrxMRE+vbbb8nGxobKly9vzK6bJe28p0+fTseOHaOhQ4dSQEAADRs2jIYNG0blypWjXr16EQCKi4ujvn37UkpKCvXq1cvIvWdCGOnMdpaLaf/8Z9KkSahWrRoKFCiAwMDAND/Tio+Px71799CyZUt4eXnxz7SyQDvv6dOnw9fXFxUqVECtWrXka65qlnn16hUeP36MFi1aoGrVqvyzzyzQznv58uX48ssvERgYiIMHD8rt2jcRSkhIQOvWrdG0aVP+aVw2fPvtt3BxccGhQ4cAAF9++SUsLS0xYcIEREZGAgA2btyITz75BM2aNeMbr2SR9jY6YcIE+Pj4wNbWFi1btsSsWbN0ln358iVu3LiBVq1aoXLlyly/s2HSpElwdHTEnj17cPLkSXTo0AH58uXDqlWr5GUWL16MqlWron79+rx9Z5H2T27HjBkDZ2dnNG3aFE5OTmjUqBH+/PNP+T0QFxeHPXv2yDeI42uvZp12XVmxYgW2bNmC+Ph4xMfHo1y5cpAkCXPmzJGXSUxMREBAADp27Mifm9kwatQoFClSBD/99JN8L5V169ahSJEi6NmzJ9auXYtt27ahSZMmOjWcM88c7ZoQFBQEPz8/hISE4M2bN7h69SratWsHT09P/PHHHwCAO3fuICAgANWqVePPzWwYNWoUChcujJ9//hmhoaEAgEuXLuHTTz9F6dKlMXjwYIwdOxYNGzZEpUqV+AafWaSd15IlS7By5Uq8ePECarUaNWvWhCRJOped1NTv9u3bc9bZMGrUKDg7O2PZsmWIiIgAkHpvMhsbG7Rv3x7z5s3DunXr0KhRI67fBhAUFARHR0f8/vvvuHfvHoDUe2WFhITAw8MDpUuXRuPGjVGzZk1UqVKF98M/IjxhznLMlClT5AmAs2fPon379nBxccHcuXPlZdatW4caNWro3AiEC0/WTJgwAYULF8aWLVtw6NAh1KlTB46Ojjh16hSA1Enc7777Dv7+/qhTpw7nnU1jxoxB0aJF0bdvX/Tt2xd58+ZFSEgIYmJiAKR+GbRx40Y0btwYPj4+vKOeDbdu3ULr1q3x22+/AUi9DmvBggXRo0cPSJKECRMmIC4uDgkJCTh27Bhf59YApk6dCicnJ/z555+4desWOnfuDAcHB/mGiACwefNm1KpVC40aNeJ6kg3Hjx9H9erVdW7EZ2tri6ZNmyJ//vxYs2YNgNQvPNetWydnzNt31gUHB+vcoHnz5s2QJAn+/v7Yt28f1Go1Lly4gKFDh6Jr1658g2YDGTVqFFxcXLBgwQJ5AuDmzZsoWrQoGjdujIULF2LlypVo1KgRKlWqxBMA2aC52eSZM2fS1OU1a9YgMDAQNjY2qF+/Pj799FOu4QYwdepUODg44NChQ4iKipLbDx48iK5du8LGxgbFixeHl5cX74dn065du+Dm5oaTJ0+meez69etYuHAhqlSpgg4dOmDYsGFcww1Au34/fvwYABAeHo7y5cvD19cXM2fORHBwMBo1aoSKFSvycU82/Pzzz3BxccH58+fl/DT//88//yAwMBDu7u5o0KABevbsKWfN23fWXL58WedLTeDtF6GvX7/GrVu3EBQUhMmTJ2P58uW8H/6R4QlzliNOnDgBb29veQLg77//hpWVFZo3bw53d3csXLgQQOoZilu3buXCk01HjhxBjRo1cOTIEQCpO5L29vbw8vKCjY0NTp8+DSD1rBYu9Nm3evVqFC9eXL7Zx4kTJyBJEvLly4c5c+YgNjYWMTExmDVrFoYPH8476tn05MkTrF27Fq9evcKJEydQrFgxuYb0798fVlZW+Prrr5GQkCA/h3fQs+7s2bOoWrWqfDb/X3/9BWtra7Rt2xbu7u6YOnUqgNSD/N27d3M9yabw8HBMnDgRycnJ2L9/P5ydnbF8+XKEh4ejevXqsLCwwA8//KDzHJ5g0Z/moEfz/zExMRgxYgR+/PFHAMC2bdtgb2+P77//HhUrVoSPj498Q+wnT57wjcoMZPXq1XB2dsbFixfTrJM7d+6gefPmqFy5Mvz9/dG7d2+eAMimvn37on///jpt72b56NEjxMfH8zZuAA8fPkStWrWwefNmuU37zPPIyEicOnUKa9euxeHDh/lzM5vmzJmDOnXqICkpSW5793Px3Wz5czPrNmzYABcXF1y8eFFu02zfUVFR6NSpE2rWrIlGjRrh888/5+OebBoxYgQ6d+6MlJSUNBPmQOq2HBsbq3Pcw1ln3V9//QVHR0fcunUrzWMZ1Q2uJx8PvoY5yxGVK1emjh07kq+vL/3111/UvXt3WrRoEbVq1Ypat25NU6ZMoefPn9PkyZOpY8eORJR6UyK+EUjW2NraUuvWrcnf35/2799P/fv3pxkzZlDbtm2pcePG1K5dO9q0aZN8TW0izjurEhMT6fXr1zR+/Hjy9fWlXbt2Uc+ePWnjxo109+5dmjhxIuXNm5c+//xzGjZsGFlaWhIR562v/fv307Fjxyg5OZnatm1LderUIWdnZwoMDCQrKyvasmULNWjQgAYOHEhEqTd5qlatGp0/f56srKzk1+HrJGadt7c3devWjapUqUIHDx6k7t2708KFC6lTp07UqlUrmj17NkVFRck3oSTi7Vtfx48fp7t379KLFy9oyJAhRERUtGhRGjt2LOXJk4fWrl1LPXr0oH79+pFSqaTy5cvTmzdvaO/evfT1118TUer9D/imcPqLj48nGxsbUqlUlCdPHsqfPz8FBgaSh4cHXb9+ncaNG0dTpkyhIUOGUOXKlaldu3Y0atQoWrZsGdWqVYuISL7vB8u6mzdvUqNGjcjHx4dUKpV8HxVJkqh06dK0Z88eSkhIIKVSKV8rXrMc05/mBp+3bt0iHx8fuU2pVJKFhQUlJyfTuXPnqFKlSlSsWDH5ebyNZw8Aunfvnk6GmnvVJCUlkSRJVKtWLbmmEPHnZlZorjf86tUrevPmjc5jSqWSUlJSaMeOHeTj4yMf72g/zrLmzp075OfnR97e3mnqt4ODA23evJkSExNJoVBQ3rx5iYjrd1Zobj559epVyp8/f5rrxr9584bOnTtHHh4e5ODgID+P67f+NNst0dtc8+TJQzY2NhQZGUlly5bVeWzbtm2UJ08e+uSTT3Reh+vJx4NnFFi2HTt2jFauXEnz58+Xd9RtbW1p9OjRZG1tTatXr6bevXtTr169yMXFhSpWrEhlypSh0NBQnZtOcuHRz9mzZ2nbtm20Y8cOua1KlSo0ePBgIiJatmwZffbZZzR48GBycXEhDw8PAkDffvstEZGcOeetnydPntCDBw/o1atXRERkZWVFDRo0oICAAAoLC6OJEyfS1KlTqUuXLvTJJ5+QQqGgIUOG0NatW+XJcgCctx5++ukn6ty5M92+fZvWrVtH48aNk+9Wnj9/fnrz5g3dvn2b8uTJQ3ny5CEA9N9//9Hs2bPpxIkTJEkS38g2kw4fPkyLFi2iqVOnUkxMDBER5cmTh4YOHUoFChSgjRs3Uvfu3emzzz4jW1tbqlixIvn4+NCrV69IrVbLr8Pb94etXLmSOnXqRCEhITRmzBhq0aKF/Ji1tTXFx8fTuXPnKF++fKRUKik+Pp4SExNp0qRJtG/fPr5RcBZs3bqVevToQX5+fjRjxgwKDw+nPHnykJ+fHzk5OdHZs2fJ0dGRunbtSkREcXFx1KlTJ6pZs6bOzfg4++y7du0aRUZGEhGRhYWFzgTA2bNn6fXr12RnZydPlvMEgH606zBRai1WKpXk7+9PmzZtolu3bunU5/DwcFq1ahXdvn1b53m8jetPs5+hvb/x+vVryps3Lz19+pSI3n5xQZS63x4SEkLx8fE6r8Ofmx/27vatmUCsXr06nT17ljZv3qzzeEJCAm3YsIFOnjwprI8fg1u3btGjR49IkiSd+q1SqejUqVMUGxtLVlZW8mQ512/9pLd9S5JEHTt2pBMnTtCePXvkdiKix48fU3BwMN26dUvneVy/9aP5QkJD899Vq1YlhUJB06dPpydPnhBRauZJSUm0fv16On36tFH6y0yE8HPaWa7y008/oWjRoqhevTqKFCmCmjVr6vxkKDk5GVWqVME333wDIPUarJ07d8aGDRvS/CSXfdjKlStRpEgRVKlSBZIk4auvvtJ5PCoqCmXKlMHq1asBpF7ypmPHjjhx4gTnnAXr1q1DrVq14OjoiMDAQPkSQxqnTp2Cl5eXfHPVq1evYvz48VizZg3/NC6Tli9fDqVSiV9//RUAEBERgfz58+PAgQM6ywUHB0OSJLRt2xZeXl6oWLGinDVv45nz448/wsXFBXXr1oW7uzvKlSuHuLg4+XG1Wo06deqgV69eAICkpCR06tQJa9as4fqdScuWLYNSqcT27dsRGRmJ48ePo1ChQrh586bOciNHjkTx4sUxcuRI1KtXD9WqVZN/9slZZ87y5cthbW2NadOmoWfPnqhcuTJ+/vlnAG+znDVrFipWrIhLly4hJiYGbdq0wbx58+TX4J/cZl5Gl8P6+eefUaZMGWzfvl2n/cGDB2jTpg2OHTsmonu5inbWJ06cwN9//43w8HAAQGhoKBo0aICqVavi2rVrSEpKwpMnT9CqVSvUrl2bL1uWRdq5RUZG6lwSZOTIkbCyssLff/8ttyUkJKBly5bo3bs31/BM0s5r06ZNWLhwIX755RfEx8cDSL1sRZ48eRAcHIwzZ87g4sWLaN68OapUqcK1O4syqgvbt29HqVKl5M9QjcePH6NFixb4888/BfQud9HO+siRI/j1118RERGBlJQUhIaGon379qhbty62b9+OlJQU3L17F23atEHNmjV5+86mBQsWoGvXrhg4cCAOHjwIIPU65g4ODqhXrx7mzp2Ln3/+Oc39VNjHiSfMWZYtW7YMFhYW8gTAgQMHULJkSdy+fVteRq1WY9SoUahatSq++OILNGjQQGdHhnce9aeZcNm6dSuio6OxY8cOKJVKPHjwQGe5wMBAuLi44IcffkDdunVRo0YNOW8+QNLf8uXLkS9fPixatAi//PILPDw8MHDgQJ1ldu/eDQsLC2zevBmXLl1Cq1at0KFDB/lx/oDVz2+//QZJknQOMgGgSpUq6NKlCxo1aoQBAwbI2/HSpUvRp08fDB06VM6Ydx4zZ/ny5XL9jouLw6lTp1C6dGlcu3ZNZ7lvv/0W3t7e6NKlC/z9/eHl5cX1O5O2bt0KSZJ0DiifPHmCChUqYOLEiejevTs2b96M+Ph43LlzByNGjECdOnXQtWtXvilcFv3444/IkycPduzYIbcFBARgypQpeP78OZ48eQIgdVKxaNGiKFGihHwzPk3mLPO09zEOHz6MHTt24OHDhwBSr1PerFkztGzZUv7S7b///kObNm1Qq1Yt3sazYcSIEXB1dUW+fPlQt25d+fr8p06dQuvWrZE3b154enqiQoUK8PX15ZvxGcC0adPg4+ODBg0aYNy4cXJ77969IUkSBgwYgAEDBqB+/fo6N0Dkz039aOc0YsQIODk5oVy5cvD09ETr1q3lL/e///57FCpUCIULF0bFihVRv359/tzMIu168Oeff2Ljxo3yPmFERATat2+Pxo0bIzg4GK9evcLVq1fRpk0bneNMlnkjR46Eo6MjHBwc4ObmhpCQECQnJ+PChQvo06cPrK2tUaxYMZQrVw41atTg+p0F2llNmjQJjo6O6NGjBxo0aABbW1v5i/wHDx6gVatW8PHxQfXq1dGlSxeuJ4wnzFnWbN68GZIkYefOnXJbdHQ0PDw8MHr0aAQGBmL79u1ISEjAzZs3MWLECDRo0EBnAoALvf5++eUXSJIk39QTSC3qvr6+mDNnDoKCguRif/fuXXTq1AnVq1fHJ598wnlnwYoVK5AvXz75bGcgdZK2W7duOH36NP755x+5/fPPP4ckSShZsqTOgSjTj0qlwrJlyyBJEoKDg+X2Tz75BEWKFMGcOXPQq1cvFC5cGC1btkz35jf8xUTmbNmyBZIkYdu2bXJbYmIiypYtiy+++AKNGjXCypUr8fz5czx8+BBTp05F69atdW7ExzuO+omLi0OHDh1QunRpbNmyRW5v3749nJycMGDAAFSuXBn58+fXubFnUlIS34gvi44cOQJJkrB8+XKd9tq1a8PLywtubm4oUaKEPJl+//59rFy5Ej///DPfqMxARo0aBTs7O7i6usLKygorVqwAAFy5cgXdunWDi4sLHBwc4OnpqTMBwHVFP9qTiQcPHkSVKlVw/PhxXLhwAd27d0etWrXkeqJWq7F161YsX74cv/zyC99sMou09zlWrlwJBwcHLF68GH369EGVKlXQsWNH+fHFixejc+fOaNeuHUaMGMF1JRtCQ0MRGBiIK1euIDY2Fps3b0bNmjXRoEEDvHjxAgBw48YNXLhwAefPn5fXE2eddaNHj4aNjQ1Kly4NSZIwa9YsqFQq3L9/HwMHDkTx4sWRP39+eHp6ws/Pj+t3JmnX70OHDqFGjRo4cuQIIiMj8cUXX8DT0xOzZ89GYmIikpKScP78eaxevRp//PEH1+9sunv3Lr799lucPHkSQOrNmr/55htIkoStW7cCAF6/fo3Y2Fg8f/6c98MZAJ4wZ1mQmJiIVq1awcPDQ2fCpX379ihSpAj69OmDunXrQqFQYMmSJQBSC41arebCkwXR0dEIDAyEs7MzTp8+Lbe3a9cOhQoVQteuXVG8eHEUK1ZMPijVPI/zzrxbt25BkqQ0Z5PXq1cP7u7usLW1RdGiRdGmTRv5sRMnTuD06dO8I5NFiYmJWLJkCRQKBYKDg9G9e3dUqlRJ59cqY8eOhaOjY5pLWLDMefPmDbp27Zrmp7Xt27dHsWLF8NVXX6Ft27aQJAnTp08HkPYgiLfvzLly5Qo+++wz1K1bF1u3bkWXLl3g5eWFO3fuyMvUq1cPlStXRkJCgs5z+WzEzHv06BE8PT1Ru3Zt+Uzyjh07wt3dHfv27cP69esRGBgIKysrXLp0Kc3z+aA/87S30+PHj8PX1xdHjx5FVFQUgoKCYGdnh/nz50OtViM2Nha3bt3C6tWrcfDgQf7czIYdO3ZgwIABOmc4R0dHY+DAgahZsybmzp2b7skSvI1n3b59+zB79mz5+CcxMRHr169HxYoV0b59e3m5V69e6TyPt+/MW7NmDapWrYpWrVrJl2FRqVTYuXMnatasifr168uT5tr4BKHM0a7fZ8+eRc2aNXHy5EnEx8cjODgYNjY2mDhxIl6/fo3ExERERERg69atOHXqFNfvbFi3bh2GDBmCkSNH6rQPGzZMnjR/9uxZmudx/c6anTt3QpIk6R0ebQABAABJREFUlClTRufXtE+fPsU333wDhUKhc6KcBu+HM54wZ1ny4MEDBAYGolGjRti2bRs6dOgAb29v3Lt3T16mdevWcHd3l3dyNLjwZN6xY8fQrVs3eHt7459//kGPHj1QsWJF/PfffwBSz2L09vZG06ZNkZiYqPNczjtzYmJiMGHCBFhaWmLTpk0AgA4dOqBcuXK4cOECrl69innz5sHGxgYLFixI83zekcmapKQkLFy4ENbW1rCxsZEPgjTXB92yZQsqVKiAsLAwY3YzV3j+/Dl69uyJOnXq4Oeff0ZgYGCa+t27d284OjoiKipK57lcT7Lm6tWr6N69O1xdXVG4cGE5V80E+fTp01G3bl3ExsYas5u5xuPHj1GxYkXUrFkTLVu2hJeXF+7fvy8/fuDAAVhbW8tnFDHDCA4ORlBQUJoJgAkTJqBAgQJYsGABoqOj0zyPPzcz78WLF/D394eVlRU++eQTnceio6Px+eefo27dupg4cSLXbQM5ffo03N3dYW9vjz/++ENuf/XqFdavXw8vL6806wLgz82sePPmDRYsWAAvLy+4u7vrPKZSqbBr1y7Url0bFStWTPNFM8uaOXPm4Ouvv8aXX36p075w4ULY2Nhg0qRJiIiISPM8rt9Z06JFC0iShKZNm+rcCwFInTT38vLChAkTeL/QQC5fvox+/frB0tIS+/fvB/C2Nj979gzDhg2DJElp7lfGGE+Ys0zTfHOvuVFTsWLFULRoUXki6/Xr1wBSr31bt25dnZvIsczR3sk+efIkOnfujIIFC6JYsWLyDqLmQ3bkyJGoX79+mrNaWOYlJCRg4sSJkCQJlSpVQrVq1XQmE8PCwlC0aFHMmDHDiL3MfRISEvDjjz/CwsICc+bMkduTk5PRokULdOjQgQ88s0lzYPP8+XN07doVxYoVQ5EiReSznTX1ZMGCBahZsyaeP39utL7mNtevX0e3bt1QvXp1bNiwQW5PTk5Gw4YN0bdvXyP2LvcJDw9H7dq1IUkSjh8/DuDt9n/jxg2UL18ef/31lzG7mOt07txZngB492SJSZMmwcHBAd9++y1PAGRBep99Dx8+RMeOHVGuXLk0N+OLjo5Gp06dMHDgQP7cNJCoqCjMnj0bRYsWRdeuXXUeS0xMxMaNG1G4cGEEBQUZqYfmK71tNCEhAT/99BNKlCiBTp066VzyUKVSYfPmzTr3t2HZM2TIEEiShFq1aqXZ91u0aBHs7e0xfPjwdL/0ZO+XUQ3u27cvXF1dsXLlyjSfmX379kWPHj24fmdBRr8yuXbtGjp16oQCBQrI+4WafCMiIhAcHMy/lmBp8IQ5yxJNIXr06BECAwPh5+eHjRs3yo+rVCo0adIEvXv3NlYXc413J827dOmC8uXL63wDmpiYiPr162Pw4MHG6GKuFB8fj5kzZ0KpVMqTt9qTjTVq1NC5BA4zjNevX2PRokVQKBRy7gEBAShfvjxfj99ANPm9ePECvXr1QrVq1bBixQqd61A2b94c3bp14x11A9OcaV67dm150rxVq1aoWLGivJPOmRtOeHg4vL2903zp2bJlSzRo0IBrSTZob6faOQ4dOhQWFhZYv359ml+8DR06FM2bN+dtPJO08333TMT79++jTZs2aNiwIdauXavzWFxcnPxczjxzMqoN0dHRmDdvHjw9PfHVV1/pPPbq1Svs37+fJ3AzSTvr8PBwPH/+XJ6UjY+Px7Jly+Dr64tu3brpTGZpP48zzxzteqD93zNmzIAkSVi8eHGaCdyZM2eiadOmXEsySXs7TU5OTvO5+Omnn6JChQpYs2ZNml9LcP3OPO28T58+jVOnTsnXLAfenrzi6OiYZtJcgyfNmTaeMGdZpilIYWFhaNu2LerXry9Pmrdu3ZonAAwovUnzSpUqyTcBDQgIQOXKlTlvA3vx4gUmT54MSZLks7dSUlIQEBCAatWq8Q56DklKSsKiRYtgaWkJe3t7ncly3okxDE39jomJQdeuXeHn54cVK1bgzZs3aNOmDSpUqMD1JIdcvXoVPXr0gL+/P0qWLAkPDw/evnNQeHg4KlWqhOrVq+P+/fto3bq1TuZcxzPv3QmAdydW+vTpg/z58+OXX36Rf3WooaknXFf0o5215h4fDRs2xKpVq/Dw4UMAwL179+RJ8/Xr17/3NdiHaee1Z88ehISEYOPGjfIvaSMjIzFnzhxUqlQJX3/9dbqvwXVFP9pZT58+HX5+fvDw8ED79u1x4sQJAMDLly+xbNkyVKtWDT169ODPyWzSzjwxMRExMTE6j48ZMwYWFhZYtmxZhvdV4fqtH+2s58yZgw4dOqB8+fIICQnRuY52x44dUbFiRaxbty7N5ynXb/1pb5fjx4+Hh4cHSpQogTJlymD06NHyY9evX0f37t3h7OyMgwcPGqOrzIzwhDnLFu1J83bt2qFRo0YoXbo0H4zmgPQmzX18fFChQgXOOwfFx8dj0qRJUCgUWLNmDTp27Mh5C5CUlIT58+ejWbNmPJmYQzT1+/nz5+jWrRvq1auHEiVK8ARuFrx7QPOhg8mrV68iICAADRs25KwFePz4Mby9vSFJEipUqMCZZ4P2tj579my0bdsW5cqVw+zZs3Vu1NynTx/Y2tpiy5YtfG8VA9Dc+Pq7777DgAEDULVqVfTt2xehoaEAUifN27Vrh0qVKulcX5tljva2OXr0aLi7u8Pb2xsNGjRA1apV5XsHPXv2DHPnzoW3tzd69uxprO7mGuPHj4eTkxO2bNmC3377DY0bN0aRIkVw6NAhAKmT5suXL4ebmxumTJli3M6aMe36rTljvESJEhgzZozOBO6YMWOQJ08erFixAi9fvtR5Da7fmRcUFAQnJyfMnj0b06ZNg7u7O/r06YN//vlHXqZTp05wdHTEvn37jNjT3GH69OkoXLgwjh49iufPn2PUqFGQJEnnV0HXr19HQEAAWrZsacSeMnPAE+Ys27Qnzf39/VGvXj0+GM0h2jspp06dQvPmzVG7dm3OO4fFx8fLZ5qXKVOG886kdycT9T1b4tWrV/I2r33tSvZ+ISEhuHLlil7Lap9p3qZNGzRo0IC372zQvuzHh9y/f1/On7POnKwcsIeFheGbb77h7dtAxo0bBxcXF8yYMQMhISGwsbHBwIEDcfbsWXmZfv36QZIkvlZ8Nm3YsAFlypTBuXPnAAD79++HQqFAhQoV0L17dzx48AAAcOvWLYwePZq/yDeABQsWoGjRojh9+jQAYO7cuZAkCSVKlJAnFiMjIzF58mT07NmTzwLNhgMHDqBKlSryZRP27t0LW1tb+Pr6olChQvIlKOPi4rBjxw7evg1g/PjxcHFxwfz587Ft2zYUKFAA3bp107ncZ1BQECRJwo4dO4zYU/O3bds2lC5dGmfOnAGQepkQSZJQunRpdO3aFefPn5eXHT9+PG/f2XTjxg20bNlS/uJh9+7dsLe3R//+/ZEvXz6dXwWFhoZy7WYfxBPmLF2ZPVtOs3xUVBRPAOQw7XVx7do1ztsA9Nk5iYmJwaZNm+ScOW/9aG+vy5cvx4sXLwB8eNKcz2DJmgMHDsDV1RWff/45bty4oddzNOsiPj6e60kmbdmyBdu2bQMADB8+HB07dkzzE+b0aG//vLOeOZrJQSA1/3ev6ayPrDyHvbVjxw6UKVNGnkw8d+4cJElCoUKF0KlTJ1y4cEFedsaMGVxPsmnbtm2YOHEigNTsCxUqhJCQEMybNw92dnbo1asXbt26pfMcnnTJuqdPn6Jz587yfSb27NkDGxsbjB8/HvXr14e7u7ucd0xMjLy/wrU8a65cuYIxY8YAAPbt2wcnJyeEhITg6tWrKFWqFJydndP8aoK376zbs2cPPDw85EvenDlzBhYWFnByckLz5s11rve8dOlSrt/Z9Ndff8n3ZNq1axfs7e2xZs0abN68GZaWlujZs6d8iVUN3r6zLiEhAYsXL8aLFy9w7NgxFCtWDCEhIQBSb6QqSRK6d++u8xyu3ex9eMKcpaFdNLZs2YKrV6/q9bx3P1C52OsnK0X63clEnlzU37///ouoqCgAqT9x1ndSURvvPOpHe9t++PAhChYsiDp16iA2NjbN49q0t+eDBw/qTL6wD/vpp5/g6+uLgQMH4t9//9XrOVy/My8pKQlDhw6FJElo37498ufPj0uXLn3wedrbt76/BGCpDh8+DH9/f/zxxx9y9prrCr8PHwwZjlqtxoEDB7B48WIAqZMv9vb22LhxIw4dOgRJktCvXz8cO3ZM53n8uamf9PbnEhIS8OTJE0RGRqJGjRqYPXs2gNQvOUuVKgU3NzdMnjw5w+ezzDty5Aju3buHS5cuoUSJEliyZAkAYP78+ZAkCZaWlrhz5468POeun/fdTFWtVqNt27YYN26c3B4QEIDixYujRYsWADhnQzh27Ji8Pe/btw8FCxbEhg0bcO3aNeTJkwddunTB/v37dZ7D9Vs/6W3fUVFRePbsGaKjo1G3bl25fqtUKpQtWxaFCxfG9OnTRXc1V8joWEVzUsSYMWPQq1cv+USWyZMno1WrVmjRogXvFzK9KYgxLQBIoUjdLMaMGUMjR46kjRs3UkJCwgefZ2FhQURE586dIyIipVKZs53NBdRqtZz35s2baeHChTRu3Dh6+PAhJScn6/Uad+7codevX+dkN3ONK1euUIMGDWjTpk305Zdf0vfff08qleqDz1Or1fJ/v3jxQt7W2ftptu0pU6bQkCFDyM3NjU6ePEmNGjWiuLg4UigUOtkSpdYSSZKIiGjp0qX06aefUkpKivC+myNNlv369aNBgwbR2bNnKTg4mG7cuPHe52nX72PHjhER1299WFpa0oIFC6hcuXK0a9cu+vbbb8nb2/u926v29r1kyRLy9fWlO3fuiOqy2XNxcaG8efPS559/TqtXr6Zr166Rm5vbBzPX1KJffvmFli5dKqq7ucK7NVqSJPL29qZPPvmEYmJiaNasWTR27Fjq2rUr1apVi0qVKkWrVq2iI0eO6DyPPzc/TK1Wy/Xh+fPnFBkZSURE1tbW5OzsTOHh4RQREUG1a9cmIqLw8HCqUaMGTZs2jSZNmkREJD+f6efNmzfyf2vXEX9/f3J3d6dTp05RpUqVqHfv3kRE5OrqSl27dqVJkyZRyZIl5eU5d/1oavGVK1fozJkzcv6FChWiZ8+e0aVLl+RcX7x4QTY2NrR8+XLau3cvEXHOmfVu/SYiqlSpEgUGBlJcXBzNnj2bRo4cSd26dSMPDw/y8PCgLVu20OHDh3Wew/X7w968eSNv35paTUTk4OBATk5OFBcXR0+ePCEPDw8iInry5AnVq1eP5s+fT0FBQUbrtzmKjY0lorfHKjt27KCFCxfSqVOn6Pnz52RpaUlv3ryhixcv0osXL8ja2poSExPpypUr1KlTJ9q3b1+6x6CMpcuYs/XMdLz7Ldv8+fPh4OCA8+fPp7lb87u0v+1funQpJEnC9evXc6SfudWoUaPg6uqKDh06oFatWnBxccHatWvT/UZfO+8ffvgBJUqU0OsMO5ZqypQpKFiwIKysrHD48GEA7z/7UDvvlStXYvjw4WlugMMyNn/+fNjZ2eHo0aO4fv06fv31V5QvXx7e3t5pzjTXznrZsmWwt7fHli1bjNJvc6V9tsWKFStQtWrV955p/m7mkiTxGf0foF0vEhIS0KtXL3Tp0gVKpRJbt24FkJrru2fCaa+bZcuWoVChQti8ebOYTps5tVot5z558mTkzZsXNWvWxO7du+Vl0qvj2usgJCQE1tbW+PPPP3O+w7mE9s06Q0ND8fTpU539krCwMFSqVEm+xm1kZCS+/PJL7Nmzh3+lkglr167VuUzQhAkTUKVKFbi7u2PixIny2XHXrl2Dj48PRo8ejePHj6NVq1bo0KEDXxIkC969hM2KFSvw1VdfYcaMGbh7967c/u2336JgwYKIiIhAUlIS2rdvj6CgIPlx3s4/bMqUKdizZ4/87xEjRqB48eLIly8fGjdujF27dsk5dunSBRUqVMCiRYvQoEED1K5dW36Mt+/M0a7fN2/exMOHD3WOX54+fQovLy9s3LgRABAbG4svvvgCBw8e5O06E77//nudf48fPx6lS5eGh4cHOnXqJLdfu3YNlStXxsiRI7F9+3a0bNkSzZo1k+s3Z66fESNG4Msvv8SzZ88AACNHjoSjoyNKly4NV1dXDBkyRL4B+caNG6FUKtGkSRNUqVIFXl5e8j4M/1qF6YsnzBlevXql8+/k5GR06dIFM2bMAID37qi8O9lSqFAhecKA6eeXX35BsWLF5J/mHzlyBJIkYdeuXWmWTS/vTZs2CeurudKebPn1119RoEABuLi44IcffkBERMR7n6exfPly5MmTh29+kwkpKSno1auXzl3JU1JScPbsWbi7u6NWrVqIi4sDoHtN4WXLlsHOzk6+NjR7v/cdRC5btgxVqlRJd9L83XpSsGBBzvwDtLPetWsXzp49i+TkZKSkpGD48OE6k+Ya736BzNt35mhn/ubNG/zzzz/4888/0bJlSzRu3DjDfQ7t52m+gOPM9TNp0iSd+jBhwgSULFkSFStWRPv27XUmcIsWLYqhQ4diy5YtCAgIgL+/P08AZMKJEycgSRJGjx4NIPXEk6JFi2LhwoWYNm0arK2t0aNHD0RHRwMAJk6ciAoVKqBYsWKoU6eOfBNbPvjX35dffon69evjn3/+AQBMmzYN+fPnx6effgpra2s0a9ZM3ge/ceMG6tatiwIFCqBSpUrw9PTkCZdMePbsGYoWLYqmTZvi8OHD2LVrFzw9PbFv3z6cOHEC/v7+qFu3rjxpe+bMGXTq1AmVK1dG27Zt5e2bJ8v1N2LECMTExMj/HjduHFxdXVG2bFk0atQI4eHhAFJvUu7u7o7evXtj1apVaNGiBWrVqsX1OxOuXr0KSZIQGBgIIPWYvkiRIli7di1++OEHlCxZErVr15bXR3BwMCpUqIAyZcrA39+f63cWDBkyBL6+vhg3bhz+/vtvNG/eHP/88w9UKhWCg4NRq1Yt9O3bF/fu3QOQuk569OiBkSNHyrWbt22WGTxh/pHr27cvunTpAuBtsX716hXKlSuHYcOGyctpP6Y5m/nds+V4AiBrgoOD0adPHwDAhg0bYGdnh6VLlwJIvT6l5nrbnHfWvHsd7aSkJCQkJGDKlClwc3PD999/jydPnqR5nmYnBnib9/bt24X0OTdp37496tatm6Z96tSpkCQJNWvW1Nm2Q0JCYGtry1nrSXv73rx5M6ZOnYr58+fLN+MDUrffqlWr6twI9N3JRK4nH6Z9QDNmzBi4ublh3bp18kRWbGwsRowYgTx58mD9+vWIiYnBJ598gn79+snPDQkJ4S8mMkF7O/3+++/Rt29fOe8bN26gefPmaNy4sU69WLJkic6ZdcuXL+ftOxMuXLgABwcHNGrUCEDqNW6dnZ2xZcsWzJ49GzVq1ICHh4d8puLq1atRrFgxVKhQAfXq1eMJgCzYsmUL8ubNi0mTJiE4OFjnS6CTJ0/CxsYGnTt3lk9w0Vxbm2/SnDWnTp1CuXLlEBgYiH379uHTTz+Vb3T48OFD1K1bF02aNMHevXsBpH7puXDhQgQHB/OESyZoasD9+/dRtWpVBAYGYsKECTpn5EZFRaFly5bw8/PT2e4jIyPl5/P2rb/r16/D3d0dVatWRUJCAo4dO4YiRYrg999/x9KlS9GkSRO4uLjIx/J79uxB6dKl4e3tjYYNG3L9ziS1Wo1Dhw7BxcUFn376KdatW4c1a9bIj//7778oV64catSoIZ8gFBoaivv373P9ziTtbXLy5Mnw8/ND37590aNHjzRXO6hZsyb69esnT5prP855s8ziCfOPmFqtls+OA95OEL569Qq9e/dGu3bt8ODBA53nXLhwAe3atcP9+/fltiVLlvAEQBZoPig///xzdOrUCSdPnoStra08WQ4ACxcuxKRJk3SK+/Lly1GgQAHOWw/aky3Tpk1Do0aNcODAAblt/PjxcHNzw7x58/D06VMAQKdOnXDz5k15mWXLlnHeesjo7J/t27ejUqVK+PHHH3XaN27ciL59+8LHxwft27cHAJw7dw4lSpTgrLNg9OjRcHZ2RmBgoHzg89NPP8mPL1u2DNWqVUOnTp106vfixYtRqFAhzjwTvvvuO7i4uOD48eM6X6xpjBkzBpIkoXLlyvD09JSX+fvvvyFJEmedBaNGjUKxYsUQEhKic6O969evo0WLFqhfvz4mTZqE1q1bw8nJSZ7MWrFiBaysrPgLuExITk7Gvn37ULFiRTRq1Ag//vgjVq5cCSB1v/H8+fOoWrUqypQpI0+a37lzBw8fPuQJgGzYvHkzLC0toVAo5NqtOcg/deoUbG1t0bVrV/ln6Bo8cZs5mm30/PnzKFu2rPyrCO0TJ+7cuYO6deuicePG2LdvX5rX4Mz1p8kqNDQUPj4+8k2BtUVHR6NVq1aoW7cufvrpJ539ST6zPHNSUlJw5MgR1KpVC1WrVsXSpUvlG3wCqRO4jRo1QuHCheV9wUePHuHZs2f8BUU2HDx4EMWKFYMkSVi0aJHOYzdu3ED58uXh5+cnf+Gvwdt35mjX3gkTJsDZ2RnlypWTTy7UCAkJQZ06dfDJJ5/wZWtZtvGE+Ufq3W+OV6xYAXd3d/nbzx07diBfvnwYMWKEfEZiZGQk2rZtiyZNmsgF/sSJE7CwsODrDOshow/FkydPomTJkpAkSWeCKyEhAa1bt9a5nMW2bdsgSRJ+/fXXHO9vbjJ69Gg4Ojpi9+7daT44x40bh5IlS6JTp07w9/dH4cKF5QmuVatWwcbGhie4PkB72967dy/Wrl0rXwc7MjIS3bt3R6NGjbBw4UKkpKTg6dOnaNOmDSZPnoyVK1eiVKlS8jVDr169apQxmLNFixahZMmSOHPmDIDUep4nTx5UrVpV50Bp3rx56NOnj7y+zp49CysrK76Odia8ePECDRo0kA+IHj58iIMHD6Jfv36YOHEiXrx4ASD10lpbtmyRd+5VKhUSEhJw7tw5o/XdXG3duhVFihSRL58AAK9fv8ajR48ApE7E9OvXD/7+/mjVqpVcv8PCwtCuXTv+vMwEzb5hcnIy9u7diypVqkCpVCIkJERnmQsXLsDX1xflypWT9xs1eAJAP+mdwblz505YWVmhX79+8q8kNMudPn0akiRhypQpQvuZG2m20XPnzqF8+fKwsbFJMzF+9+5d1K9fHz4+Pjh69KgxumnW0tu+w8LCUKNGDXh7e6e5l0R0dDRq1qyJQYMGiepirqN9L4PDhw+jXr16UCgUaa6xfePGDTRu3BhFihRBaGiozmNcv/Xz7vb95s0bHDx4EKVLl0bTpk3TLHfz5k3Y29tj4MCBQvuZW2S0XU6fPh2lS5fGqFGj0lxidc6cORg4cCBv0yzbeML8I/Xu2RHHjh2Dj48PqlWrJt+Ib/369ShWrBiqVq0KLy8vVKtWDd7e3jrXk3v8+DEuXbokvP/mRrtY79ixA7Nnz8bSpUtx4sQJAMDXX3+NcuXK4dtvv8Xz589x+vRpBAQEwMfHR+eb/levXuHvv/8W3n9zdvjwYZQuXRpnz54FkHqt7KdPn2LHjh3yjkxwcDAGDRqEvn37ynknJiZi6NCh2Llzp9H6bm7GjBmD/Pnzw8PDA5IkYerUqfLEVv/+/VG6dGkULFgQZcuWRYUKFQCkrp+SJUumuQEXy5h2PXn9+jXGjh2LOXPmAAB+++032NvbY/LkyWjdujXKlCmjc3a/ZptXq9WIioriGzRnUkxMDPz9/TF69GisW7cOHTt2RP369VGnTh34+PhgwIABac7O4rMRs2fmzJlo3rw5AODy5cuYO3cuypcvD3t7e0yfPh1A6uXL4uLi0lx7NTIy0jidNkPatQFIrS2///47KlWqhGrVqqVZ/uLFi3B1dUXnzp2F9jM30K7h8fHxOo9t3rwZFhYWGD16dJrLI1y7do3P/syijCZNLl68CA8PD7Rt21bnUmYA8N9//+GLL77gCZdM0s7r/v37ePnypfxrlHv37sHHxwdNmzZNczwTGxvLWWfRu/VbpVLh77//hp+fH0qXLi0f22vcvHkTXl5eaN26tfC+mrt367fmHkxqtRoHDx6Ek5OT/MtZTTsAPHjwgPcHs0A776NHj+Kff/6R7/sGpJ74VrVqVQQFBcm/FtfgG2IzQ+AJ84/QoUOH5MtS9O3bF0OHDgWQOnFVrVo1+Pj4yB+sp06dwvr16zFmzBj89NNP8o4677BnzahRo+Dm5oZWrVqhffv2sLe3x86dO/H48WMEBQXB1dUVtra28PLyQpMmTeSDJZVKxZln0Z49e+Dq6opXr17h33//xbhx41C6dGnY2trqTAJo58s3GdKPdj7nz5+Hn58fTp06hcTERCxfvhw2NjYYPXo0Xr16hVevXuHevXtYsmQJfvvtNznvIUOGoF69enj+/LmxhmG21q5di8uXLyMsLAyPHj3CrVu34OHhgfnz5wMA/vzzT9jZ2aFUqVL45Zdf5OfxtSn1874zWqpUqQJra2uMHz9ePvtwwIABfPZQNqW3bf7666+QJAm9evVC2bJl0aVLFyxevBhz5syBJEk6l2gBUtcbb+OZo72tJyYmypO4SUlJ2LdvH8qWLYsGDRro5KpWq3Hr1i2eAMgk7QznzJmDTz75BO3atcORI0fk3Ddt2pThpDnA++CZpZ3dtm3bsGjRIvz555/yz/j/+ecflC1bFoGBgWkmzTV4fzDzNDeoLVeunM4vlu/cuQNvb280bdoUBw8eTPM8zjpztPN6+fKlfINJlUqFo0ePomrVqjrH9hoPHjzgrLNh+vTpaNmyJapXr47du3fLN8PWTJprbgT6Lv7M1J927R4xYgScnZ3h5OSEGjVqYPHixfJjmknzCRMm4PHjxxm+BmNZwRPmHxG1Wo34+HhUqlQJDRo0wKeffgp7e3tcvHgRQOoH7qFDh+RJ83d/ZqvBhT5rtmzZgqJFi+LUqVMAgB9//BFKpRKrV68GkHo21/Pnz3HgwAH8999/fC3QLEhvx+/WrVvw8fFB+fLlUbhwYfTv3x+rVq1CaGgoLC0t+XJCWfDur0q+//57fPHFF2l+SrtixQrY2Nhg7NixaXZgzp07h6FDh8LOzo5/paKnd2+AWKBAAVy/fl2uERs2bEDVqlXlayTu3r0bbdu2xfz58/mgKJO089q2bRsWL16M6dOny9txREQEbt++rfOcJk2ayF9As8zTzjwmJgZJSUnymVvLly9H06ZN8eOPP8o/IQ8NDUWtWrX41ykG9O2336JZs2bw9fWVPxtVKhX27dsHT09P+Uag7+L9Qv1ob+Nz586FnZ0dxo4dK9/vYNGiRfKk1i+//IJ8+fLh888/53yzQXuyZOTIkShcuDDc3d3h6emJzz77DA8fPgSQOmnu4eGBjh078iVYskh7+96yZYt8s+ChQ4eiUaNGaNGihXzZvbt376Jq1aqoUqUKzp8/b6wu5ypTpkxB3bp1UaFCBZ37YR09ehTVq1dH1apV00yaA1y/9aW9fc+bNw8ODg6YNGkS2rZtCysrK8yZM0c++efgwYNwcXGBv7+/sbpr9rRr96VLl+Dp6YmzZ89i//79GDVqFFxdXeVf1wKp1zR3dXXF8uXLjdFdlovxhPlHKCEhAcWKFYNSqcSKFSt0HtNc96xGjRo6l2dhmffuBNXMmTPRs2dPAKk3QrS1tZWLelxcXLrXbuZJLv1pZ3X27FkcOXJE/nLiypUr+Pbbb7Fz5075zIvIyEjUqFEDhw4dMkJvzVe3bt10rqsPpF4jXpIkVKtWLc0lEH788UfY29vjyy+/1HlszZo1CAwM1PlZHdPPjRs3MGXKFPnazJqdyk2bNsHDwwO//fYbXr58iTZt2iAoKCjNZSqY/oYPHw4nJyfUq1cPzs7OKFu2LEJCQuSfl7948QJnzpxBQEAAKlWqxF9wZpH2gdF3332HJk2aoHLlyujRo4c8maK5pnNKSgoSExMREBCAhg0b8udkNmhnN2fOHDg7O2P8+PHo3r07FAoFZsyYAZVKJU+aV6pUCZUrVzZij83Tu2e4/fvvv+jXr5/O2bX9+/eHt7c3fvjhB3nfe9WqVfD39+cz5LJIO7fLly+jTZs2uHDhAhISErB06VL4+/ujffv28qT5mTNnYGdnh6CgIGN1OVfYt28fRo4ciVWrVsltW7ZsQdOmTdGsWTP5eOe///5Dr169uIZnkXZuwcHBcHFxwYwZM/DVV19BqVRi+PDhePXqFYDUSfNatWqhWLFi8pnQLGtu376NYcOGyb/WB4Bp06ahQIECmD17tnycuW/fPrRs2ZK372z66aef0Lt3b4wcOVJue/DgASZMmIBixYph7ty5cvvy5cv5WIcZHE+YfyQ0xVqlUuHRo0fw9fWFp6cnmjZtmubGK5pJc1dXV/Tp08cY3c1Vdu/ejfDwcMyYMQPDhg3Djh07YGNjI99ES61WY8uWLZgyZQp/QZFF2gdFY8eORcWKFVGyZEn4+vqiVatWOssmJSUhIiICbdq0Qc2aNfmDNZPu3bsnn/V5//59uX3u3LmQJAnz5s2TJxM1goOD0aRJkzQH/Rn9ioVl7PDhw5AkCdbW1mluRnvz5k20aNECrq6uKF68OLy8vNL9OT/Tz/bt21GkSBFcunRJ3ub79euHatWqYe3atQCA33//HQ0aNEDr1q11LqHFsmb8+PFwcHBASEgIxo4di7Zt28La2hqHDx8GkPpz83Xr1sHf3x9VqlThy2cZyK1btzBlyhTs379fbgsJCYEkSfj222/lSfPffvsN3bp14208k7RvNr5x40a4ubmhbNmyaW4CPGDAAHh7e2PhwoXypIsG1/Cs27RpE5o0aYKOHTvKNQNI/UKiXr16CAwMlCfNr1+/ztt3JmmfIX7mzBn4+PigUKFC8i9oNbZu3YpmzZqhRYsW8o3hNbiGZ92VK1cwa9Ys7N69W27btm0blEolhg4dKn/ZfODAAfTr14+370z6999/5f/es2cPJElCkSJFdD4vgf+xd99RVR1bGMC/Q7EiIBZUiihFQFQURVFAMaKAYsEeNfZujIXYFY0x9i4KiIXYsCEmlsTejb2LPfYaRRQVRfjeH6x73r1BE0ATxOzfWm89Offc62QY587sM7MnbYeWqakpJ0+erO701JD2nXGxsbFq9oMHDx6wTZs2NDMzY5s2bXTuu3nzJkeMGEFra2uGhITovCZtXHxMEjD/D9DupLdu3aoGZR8/fkw3Nzf6+Phwy5Yt6QbjcXFx0uFkgaaTJ9Py91lYWPDq1atctGgR8+XLx1y5cqnBcjItaFi3bl0OGDAgG0r7edFskTt48CCTk5M5duxYKoqiriJ//fo1Fy1aRB8fH7q7u0uAK5O0J5phYWH08PDQWSE3evRo6unpcebMmemC5toHEsnEP+PeNcieOHEiFUXhqFGj0q1ovnjxIjdt2sQlS5ao7VpWPWfNnDlz6ObmxsTERLUOU1NT2axZM1asWFG978iRI5JC6yO4desWXV1duXbtWvWa5sDgQoUKMS4ujk+ePOH8+fM5YMAAOVPlA5w7d07th3fu3ElFUWhmZsaNGzfq3BcWFqaz0ly7P5LvzYyZN28erays1MPI3rx5w6CgIObKlYszZsxgUlKSzv3du3dnsWLFJF3cBwgNDVV3cKampqpn1zg4OKTrLxYtWsRatWrR29tb58A4ad8ZM3fuXCqKwosXL+pcK1OmDGvWrMnbt2/r3L9mzRpWrFhRTV8mgcTMO3nypNpvHDp0iIqiMG/evOn6jLVr19LAwEBnpbmGtO+MCQsLo52dnc4CoW+//ZaKonDSpEnpVuuPGzeOiqJw2bJl/3ZRPwvh4eFUFEUnLdbp06fZpUsXFihQgEuXLtW5/9atW+zbty8bNWok80vxj5GA+WfuzytvnZycOGvWLDVofuvWLVaqVIm+vr7csGED37x5wxo1anDEiBHq++RLNePmzp1LZ2dnPn36lHfv3mWvXr24efNm9fX+/ftTURSuWLGCJ06c4OnTp1m3bl1WrFhRJyAjMubPddWuXTtGRkaSJNevX09jY2M17ZBmsLh161bOmjVLgi0fID4+npcvX6aDgwMbNmyok9YmJCSE+vr6nD17droV5NK2s27p0qXcu3ev+vPo0aOpr6+vs+X5XaT/zjzNBH7ixIm0t7dXr2v6kMuXLzNv3rzct2/fO98nsubixYvMnTu3zncmmbb6uVq1ampOVs3BiKS076yYO3cuy5Urp+aCJ9NSximKwsmTJ6frpyMiIqgoSrrVouLvhYeHU19fX02fpf1grUGDBixfvjxXrVql7mDRmDBhgrTtLNK0V+0dWMnJyZwyZQrt7OzYrVs3Pn36VOc9c+bMYa9evaQPz6SwsDDmzp1b5yGnxty5c+nh4cH27dvzzp07Oq/t3LlT6jqL5s6dSwcHB50zVCIiImhgYMChQ4em6zfWrVtHRVE4c+bMf7uoOZ4meLtu3TqSunOYXr16MU+ePFy6dKm6gl9j8eLFMrfMgrCwMBoaGjI2Njbda6dPn2bXrl3p5OTEFStW6Lz24MEDnUVZQnxsEjD/jxg1ahQLFSrEffv2qSs/NZ3KzZs3Wb16dZYtW5YODg4sV65cusG7+Hua7csxMTHqlq1SpUrxt99+07mvR48etLKyopGREd3d3enj4yMrnbNAe7B9+vRpvnnzhu7u7ly0aBF/+eUXGhkZqQGWt2/fctKkSem+hKW+MyYmJkYdMA4YMIBdunQhmbZN0dnZmfXr19cJmo8ZM4aKonD16tXZUNrPz/Pnz1m4cGF6enrq9CcjR46kvr4+Fy1alH2F+wy8b+J++/ZtmpiYsHv37jrXDx8+zDJlyjAuLu7fKN5nSXtSo/nzq1evWKtWLfbv3z9derJq1arJgaofgSYA8K4A14gRI2hgYKCmG9IWGxsrAYBMCgsL0wmWa2hW4iYnJzMgIICurq7vDJqTMkbJLE2dvyvg8ubNG/7www/08PBgr1690vUxmn5IArkZ8+eHQRrbt29X/zxnzhzWqFHjnUFzUuo6s8LDw6mnp/fO/nvGjBlUFIXTp09Xr2na9K5du6T/zqSwsDAaGBikq2vtXeQ9e/Zk3rx53xk0J2VBVmbMnz//ncHyH374Qf3z6dOn2a1bNzo5OTE6OjrdZ0iwXPxTJGD+GVq6dKl6SjOZlnO4SpUq/OWXX0iS9+7d46FDhzhgwAB1+9a9e/cYFRXFefPmycrbLFi5ciUVRVFXHD579oxffvklFUV5Z6d++vRp7tu3j2fPnpWt/Fmg/aU4aNAg+vr68tq1a/z6669Zu3ZtmpiY6KS9uXfvHuvXr885c+ZkR3FztOfPn7Nbt240NDRkkyZNmC9fPp0B4/uC5gsWLJA2nUXvGvTdvn2bTk5OrFWrlnqYLZkWNM+dO7e07SzSnrDHxsZyypQpjIqKUut4+fLlLFCggHr45NGjR9mgQQN6enrKZD+LtOstKSlJJ3A1ZswYlitXjuHh4eoE9MWLF6xevTonTZr0r5f1c6IJAPw5wKWdn3X48OHvDZqTMk7JqOjoaCqKwt27d+tcb926Nfv27atu409OTmb9+vXp5ubGqKgoqd8PEBERwTx58qRr3+3bt1fHJsnJyWrQ/Ouvv0630lwCLhmjWbWsHRwnycaNG7Ny5co6ffqcOXPo7e3NwMDAdIfCi4wLDw9/ZwD35MmT6oO1mTNnUlEUTps27Z2fIf1LxqxevZqKoqQ74y0oKIhff/21zi63Xr160cjIiBEREbLYMIt2795NRVHUNFoazZo1Y9GiRXn//n312unTp9mjRw8WLFhQ59BVIf5JEjD/zISHh7NevXo6E9I//viDNjY2nDhxIo8cOcK2bduyQoUKdHd3p6Io6fJBkbKqJTPmz59PRVGop6enc4jN8+fP2ahRI5qbm/Pw4cMk3z8Yl8BL1pw7d47Vq1dXH1QcPHhQXbmvWcV19+5dBgQE0MPDQ9p1Fv3xxx8sU6YMFUXhjBkzSKYNvDWD7/Pnz9PFxYWBgYHqgzkNGaBnnWYyr+k37ty5QwcHB3p5eemsNO/Xrx+9vLxksv8BgoODWaxYMbq7u7Ns2bIsVKiQGjTcsGEDS5UqxeLFi9POzo5eXl5y2GQWadfX+PHj6evrSzs7O3bq1En9/uzduzfLli3LOnXqMDg4mJ6enixbtqz0JR9A81B//fr1OtcbNGjAPn366OTRHjFiBPPkycOwsLB/u5ifhcePH7NJkyY0NzfX6aebNm3KMmXK8MaNGySpszilSpUq7NChQ7aU93Nw6dIlKorCbt266Vxv1qwZy5Qpo3PoanJyMidMmEBbW1tOnTr13y5qjvf27VuGhYXpjAfJtGBiuXLl1FzP2uPtSZMmsXv37vJ9mUWrVq1Kl2aIJAMCAtipUyed1c2zZs2ioaEhx4wZ828X87Pw7NkzNm3alLa2tjq7ZDX9t6Z9a49HWrRoQR8fn3+9rJ8LzYKg6tWrq8Hxpk2bsnz58mp9/3lXuaQtE/8mCZh/hjQdyIEDB3j37l2S5LBhw1iqVCnmypWL/fr1Uw92atiwIfv27ZttZc3pNHnjZs2axW7dutHMzIy7du1SX3/x4gUDAgJYokQJHjlyJBtL+vn54Ycf2KhRIzZt2lTn0JVt27axaNGidHNzo4ODAz08PFi5cmVJe5NJ2oOThw8fsk2bNmzatClNTEzUFS4pKSnqiooLFy6waNGicnjtRzJlyhTWrFmTV69e1bl+9+5dWllZ0dvbmwcOHFCvS/6+rIuJiWHhwoV54MABpqam8tq1awwJCaGenh6XL19OMi1/+fHjx3nmzBnZFfQRjBgxgoUKFeL48eM5Y8YMOjk50dPTU81fvnjxYnbu3JkBAQHs06eP9N8f4NWrV6xfvz4dHBx0Ai7NmjWjk5OTTi5zjd69e9Pb21v6kyzau3cvv/zyS1aoUIGHDh1i27Zt6eLiota1pl61D2aWYGLWxcfHc8SIEcyVK5ea3zYoKIguLi5qwEW7LScnJ+scjC0y59WrVwwNDaWenh5nzJjBNm3a0MXFhdeuXSOpm95GM0aUlDdZk5yczNatW7N06dI650g0bdqUzs7Oap1rGzt2LD09PaX/zqLTp0/zq6++oqenJ1evXs1WrVqxXLly6dq3dv8h7frD3L17l2XLlmXVqlUZEBDA8uXLqw+Xtec32jtsSRkTin+HBMw/I5pOIzU1lTt27GC+fPk4fvx4JiYm8uXLl4yLi9NJpZCSksIaNWrINucs0mzZ+umnn0iScXFxbNeu3TuD5vXr16eVlRX379+fXcX97CxevJiKorBYsWK8cOECyf9/qZ49e5bR0dEcN24cY2JidCal4u9pD/x++eUXnjt3jklJSXzw4AF79epFY2PjdNtCX758yfv378vg5SM5ffo08+bNyyZNmqhBc83vZfXq1dTT06O7uzvPnDmjvkcmR39Pu4409al5OKEtPj6eAwYMYMWKFXVWJ/75vSLzrl69SkdHR/W7k/z/TiBPT0/eu3dPva7dZ0v/nXU3btxgkyZNWLt2ba5Zs0ZdvfXnAID2n+UhXOZp19WBAwfYsmVLFixYkBYWFuqDfe3vyLp16+rkbJV+JetevHjBkSNHUlEUuri40M3N7Z3t+88H8smYJWtev37NWbNmMV++fDQyMlJ3xGnXZ61atRgSEqL+LH1J1jx58oTt2rVjjRo1uGjRIjZp0uS9/bdmtbn03x/mzJkzbNOmDS0tLVm0aFH+8ccfJHXHIQ0bNlR3WaSmpkr//YHu3LnD6tWr66S41ebt7c369etLmxb/OgmYfybe1XkMGjRITcWinf8pMTGRx48fZ0BAACtUqCCT0Cx6/vw5d+zYoXPtwoUL7w2aV61alQ0bNvy3i/lZeN8gJCYmhoqisE+fPn+bG1EmRRmj3ZcMHTqUVlZWXLZsmXpY8LVr19irVy+ampqqZyA0atRIZ2W51HXm/Ll9a+rvzJkzNDY2ZsOGDXVWmq9atYqdOnViq1atpK4z6V31tWDBAhYvXlxdzaKxYcMGmpmZyeGeH9mNGzdobW2tribXrB5/8OABCxYs+M78qzJByjpN/3Ljxg0GBgbSwsKCFhYW6oMg7TFgq1atdNJUSL1n3p+D5q1ataKjoyP37NmjXk9JSWGDBg1obW2ttn/x4RITEzl+/Hjq6+tz8uTJJHX7/C+++II2NjYS2PpIXrx4wfnz59PAwECtb/L/7btUqVLSvj+Qpv0+efKErVu3poWFBYsXL84rV66Q1O2/mzdvztGjR6s/S//9Yc6dO8cvv/ySVapU4bJly9Trb9++ZUBAAB0cHKR9f2R37txhhQoVWLlyZZ3dEwEBASxTpozUt8gWEjD/DGh/Ia5Zs0bnkMkhQ4bQysqKEydO5IMHD0iSK1asYKNGjVi7dm3Z5pwFqamp6epL++eLFy/yq6++opmZmc4hiElJSTJIzwLtOjt37hwPHjzI27dvqzlXf/zxRyqKwkGDBqkrAEgZKH6o7777jubm5ty3b59OyhsyLUXL119/TUVRWK5cOdrb28sgJou023d0dDTHjh3LYcOG8dChQyTT2ryxsTEbN27MX3/9lffu3WPDhg0ZGhqqvk/674xZv34927VrxxYtWugEBQ8dOkRXV1eGhITwzp076vVz587R2dlZZ2eWyJzr16/zxIkTOofAPXz4kBYWFurKw5SUFHXSX7duXQ4ZMiQ7ivpZ0/Qzt2/fZpMmTejh4aGmGyLT+hB/f3/a2dlJX/4RvCto7uLioh4C6u/vrxNskYUrH8/Tp08ZEhJCRVG4aNEikmm/D39/fzo5Oal1LmPEjyMpKYmzZ8+mnp6eGjSX9v1xafrvp0+fsn379qxcuTIjIiJ05vD+/v60tbWV/vsj06w0r169uho0b9CggU7wVtr3x3Xnzh26uLiwSpUqvH79Ohs0aCD9ichWEjDP4bSDLSdPnqSTkxN9fX11Dt4bOnQora2tOXHiRD579oyPHz/mjh07JE1FFmjXd0JCgs7P2oOUixcvsn379ixSpEi6QxAlaJ5x2hOawYMH08HBgfnz52eFChXYvHlzNQijCZoPGTKEDx8+zK7ifjYeP35MLy8vRkREkEwbvOzbt489e/bknDlz+OTJE5Jp+eLnz58vfclHEBwczJIlS7Jx48Zs06aNzoHMcXFxLFu2LC0tLWlhYcFKlSrJpCiTwsPDaWxszO7duzMoKIilS5fWWTE0evRoOjo6sm/fvty+fTvPnDnDunXrskaNGtJnZ1FUVBSdnZ1pbm5OW1tbbt++XX0tMjKSBgYGjIyMVK8lJyfT1dVV0sT9QzTt+ObNm2zYsCFr1qypBs0DAwN1AgDyEO7DvSto7urqSmdnZ5n8/8MSExM5atQo6unpMSoqis2aNZM6/we9fv2as2fPZq5cuWhqakpHR0ep649M03/Hx8ezdevW9PDwYEREBJOTk9mwYUMJ4P6Dzpw5w7Zt29Lb25s2NjbSl/wL7t69ywoVKlBRFDo7O0t9i2wlAfPPxNChQ9mxY0e6uLgwd+7c9PLy0skPOmzYMNrY2HDEiBF89uyZel0CAVnzww8/sGrVqmzQoAGnTJmiXv9z0DwwMJD+/v7ZUcTPgqZ9Tp8+nWZmZtyyZQtPnz7NOXPmsFq1avT29lbb8/Lly6kois7KW5F5qampvH//Pp2dnTlx4kSuXbuWrVu3Zo0aNVihQgW6urpy1KhRf7nLQmSMZuAXExPDEiVK8PDhwyTTUoEoiqIT0L179y63bt3K2NhYeUCRSZrgbExMDMm01B81atTgypUr1VRDJDljxgzWrl2biqKwfPny9PDwUPt0+a7MnLCwMObOnZthYWE8c+YMa9asSXd3d/X1xMREjhgxgoqisHXr1uzTpw9r165NZ2dnadf/IO2guWanoa2trQQA/iHaQfODBw+yXr16rF69utT1vyAxMVFdaa69c0Lq/J/x+vVrTps2jXXr1pW6/odo+u8nT57wyy+/pJeXF0uWLCn9dxb8eUz3dztOzpw5Q39/f/r4+EhdZ0FWdvTcvHmTffv2lfoW2U4C5p+B0NBQGhsbq6kq9u7dy0qVKtHPz48bN25U7+vTpw+bNGki2xCzQPuLNTQ0lGZmZvzhhx/YokULuri4sGvXrurr2kHzmzdvSqAlC44ePar+OSkpKV1evuTkZG7atIlubm4cPny4Wsdbt26VL9RMel/7HD58OC0sLJg/f34OGTJEzdffrFkz9u7d+98s4mdny5YtOv3w7Nmz2bFjR5JpB3oaGRkxPDycZNoW3OvXr6f7DHlAkTGxsbFUFIWzZs3SuV6xYkVWrlyZJUuWpK+vL48dO0Yyrb5PnDjBs2fPqv82pE/JnAULFjBXrlw6448tW7awSZMm/Pnnn7lz5071zIn169czICCAQUFB7NGjh1rX0r7/OdpBc29vb3p5ecmE9B+k3ddLv/LxZKSPiI+P54oVK9S6ljrPmD+PCzM6j3n58qXa3mUXXMbNmzePp0+fztC92ivNAwMDWatWLem/P4B2nuy/c/36dem/s0D7fKBVq1bx9evXmf6MrLxHiI9FIUmIHK1Lly6Ij4/H2rVr1WsHDx5EmzZtUKJECQwbNgwBAQEAgJSUFOjr64MkFEXJriLnWDt37sSRI0fg7OyMBg0a4NmzZ1ixYgWmTJmCmjVrIjIyEgDw5s0b5MqVS31famoq9PT0sqvYOUp4eDh69uyJuLg4lClTBgBQt25dmJmZITo6Wuferl274vr16/j111916vft27cwMDD4V8udE2m3y/Xr1+Px48d49uwZOnXqBGNjY1y6dAkk1d8DkPa7cHNzw/jx47Or2DnakydP4Obmhjx58uD8+fNQFAWjR4/GyZMn0bZtW3Tq1AkTJ05Ez549AQBLlizBgQMHMGnSJBQoUCCbS5/zrF+/Hu3bt0enTp3Qt29f2NjYoGnTpjhx4gSGDh0KfX19jB8/HgULFsT+/fthaGio837puzPn+vXrqFGjBmxsbLB//371eu3atREXFwcDAwOkpKTA0tISK1euRKlSpZCcnKxT79J/Z96f2+nfjfE09z9+/BgFCxaEnp6e1HsGZaVP+PPvQ8bgmRMXF4eiRYuiUKFCGDp0KNq3bw9HR8dMfYa074zRbpsRERFo2bIlTExM/rbdS5vOmm3btqFjx46oX78++vXrl6F2rfldvHjxAnnz5pX+OxNWr14NPT09NG3aFAMHDsTNmzcRFRWFfPny/eX7tNu/jAszbvfu3Rg1ahSGDRuGX375BTNnzsSNGzdgZWX1l++TOhaflGwL1YsPpnnK2bt3b/r5+ZHUPZBywYIFzJcvHxs3bsytW7eq75MV5lmzd+9eWlpasnDhwty7d696PSEhgeHh4XRwcNBZaS4yLywsjAYGBly3bp16LTU1lSEhIaxatSqPHDmis9Jlzpw59PT01EmrIDJvwIABNDc3p6urK4sXL05ra2vGxMTw1atXJNNW3R45coT169eni4uLrKz4AKmpqdy/fz9dXFzo6urK1NRUHj58mE5OTsyTJw+nTZum3vv8+XM2aNCAffr0kX77A6xevZqWlpb85ptvWLduXZYvX15n1f7GjRupKIpOjm2RNfHx8YyIiKCFhYW6E6VFixZ0dHTkiRMn+OzZMy5cuJAlSpRgSEiIzoGfpIxPskL7O3HVqlU8c+ZMht73535cVvX/vT8f0jxz5kwOHTqUN2/e/MsVcNrt+vLly3z58uU/Ws7PyalTp1i0aFHOnj2bvXr1oqIoGWrj2r+r+Pj4f7CEnw/tOrt16xYLFizIGjVqqOcFvW+luXb73rFjB48fP/7PFvQzExkZSTc3N3br1o3nz5/P0Huk/868169fs1+/flQUhY0bN2b+/Pl58uTJv32fdvvO6E4AkebChQv09fVlyZIlaWpqynPnzpH86/aqXd8rVqyQVKsi20nAPAd530Bl9erVVBSFK1eu1Lm+ZMkS1q9fn5UrV1a3+4usu3nzJkeNGkUzMzP27dtX57WEhARGRETQ2NiY48ePz6YS5mwLFy6kvr6+zjZ+knz48CEfPXpEe3t7+vn5cefOnXz16hUTEhJYu3ZttmnTJptK/HlYsWIFixQpwlOnTqkH2TZt2pSlS5fmtm3bSKbl1K5SpQr9/PzkULiPICUlhQcOHGCZMmVYtWpVkuSIESNYpEgRjhs3jmfOnOGBAwfo5+dHV1dXdWIkwcTM0a6vlStX0tzcnCYmJmq71ti2bRsdHR159uzZf7uInxVNn5CQkMBFixaxaNGiNDc3Z8WKFXn//n31vpSUFJYrV05SO30E2m180KBBtLa25tChQ5mYmJjh9x05cuQfK9/n6ttvv6WlpSWbNm3KatWqsVixYvzxxx/f+TBZu65nzpzJkiVL8ubNm/9mcXO80aNHs2DBgsybNy937dpF8q/ThGjX+YIFCzhgwABZWJEJISEhDAoKYvny5akoCt3c3N4bNNeu69DQUBYqVEj6lAzSrsv58+ezYsWKGQqaa9f5nj17/rHyfa4cHR2pp6enLlDJaPB2zpw5NDQ05OXLl//xMuZ0qampavsOCQlh7ty5WbVqVW7YsEG95119uHZ9z5s3j/ny5eOvv/76zxdYiL8gAfMcQrtT2bZtG1evXs2ffvqJSUlJJNMmSrly5eKiRYt45coVPn78mIGBgYyIiOD69eupKIoEAzLhfQPx+/fvc/To0bS1teWoUaN0Xnv69CnXr18vgcQs2LdvHxVF4bfffqtzvVmzZhw8eDDJtBxoFSpUYPny5WllZcUqVaqwXLlyagBXgolZM3nyZNasWZNv3rzRmezXq1ePlSpVUn8+ePCg5O7LokOHDnHTpk0kqZNL9dChQyxVqhS9vLxIkiNHjmTFihWpKAqrVq1KX19feUCRSX8VRImNjaWFhQX79OmjrnIhycDAQPr5+cl5E1m0a9cuTpkyhd27d+eLFy9Ipu2OWLRoEUuVKsWWLVuq96akpDAhIYE1atTg5MmTs6vIOd6f2+q0adNYqFAhHjt2LFPB8rlz51JRFJ1/D+KvRUdH08LCQl1puHv3biqKwp9++indvdp1HRYWRjMzM65YseJfK2tOph1wiYmJoYmJCYsVK8aZM2fy3r17f/k+jfDwcBoaGjI2NvYfL+/nYtq0aTQ2NuaePXt47tw5xsTE0NHRkRUqVEgXNP9z+zY1NeWqVauypdw5lfbYLiIigpUqVfrLoPmf61xRFFnR/ze0vy9fvHjB9u3bs1WrVtTX1+fq1atJptXrn+eR2r8bTf/958WJIj3t+tbMdX799VcGBATwiy++UOv8r96n6U/WrFnzj5dXiL8jAfMcJjg4mNbW1rS2tqaNjQ1tbGzUiU5ISAjz5cunvu7s7MykpCSeOHGCdnZ27zw4TqSn3WH/+OOPDAkJ4ddff83Dhw+TTDudfPTo0SxTpky6oLmGBLcy58WLF/T09KSnp6caWGzZsiXt7e112u0ff/zBTZs2cfLkyVy8eLEc5PQBNAPDYcOG0d7eXr2u2Sp+9OhRFi5cON12RQkqZs6OHTuoKAoVRWG1atXYoUMHrlu3Tj0E59ChQ3R1dWWNGjVIph2UtWvXLt64cUMeUGSSdtuMiYlhWFgY582bxydPnqjtXRPs6tOnD+Pi4hgYGEh7e3v1wYS078xZvHgxHR0dOXToUEZGRuq89uTJEy5atIjm5ubs0qWLet3f358VK1aUdp1Ff07n8ebNG7Zq1Yrjxo0j+f/xx9+t3tIEAN43eRXvNmPGDHXX5rJly2hsbMy5c+eSJBMTE/nHH3+QTB9sMTY2lsl/Bv05Ncjr16/54sULjh49mlZWVpw4caLOrhUN7YMmNXW+du3af6XMn4OUlBS2b9+effr00bl25MgRlipVitWqVeOzZ89I6h7CJ+07c/5qnBEWFvbeleZ/7r8LFiwodf43tOv6p59+4pEjR/jmzRumpKRwwIABOkFzjT8/QJb2nXHa9T1x4kR26tSJjx8/JknGxcWxXr16/OKLL3T65dDQUDUFKJn2oFPqW3xKJGCegyxcuJBmZmY8fPgw7969y3PnztHf358WFhbq9s7ffvuNP//8M2NiYtTB+sCBA+nq6qoO4kXGDBw4kEWLFqWvry/d3d2ZO3duTpgwgS9evOCjR484ZswYli1blv369cvuouZomqBJYmIia9euzWrVqtHDw4Nly5bVWUX0vgGmPJzImPfV3+XLl1mkSBF+8803Otf37t1LBwcHXrly5V8o3efrypUr9PDwYOXKlenn58e+ffvS1NSUtra2bNiwIWfMmMHFixfTwsKCderUSbfCRQK4GaNdb4MHD6a5uTlr165NMzMz+vn5ccuWLTpB85IlS9LIyIhly5ZVgywSwM2cJUuWMG/evFy+fLk6ISLT0gvFxcWRTNt5tWjRIhYrVozdu3dnkyZN6ODgIDsnsqhTp05s1aoVyf+3+ZcvX7JMmTLs37+/ep/2a5rxoQRwP4ymL+7evTtbtGjBAwcOsECBAmqwnCRnzZrFUaNG6fQl4eHhNDExkbrOIO3vvO+++461a9fWOYdp+PDhtLKy4tSpU/ngwQOSaeckXLhwQb0nLCxM6jyLGjduTE9Pz3TXx4wZo+5+0+5L5s2bxwIFCsiDiQzSbt8rV67kmDFjOG3aNP7222/q9bCwMFaqVIndu3dXv0v/vPpW+u+/9+dxoZWVFZcsWaKOVxISEjhw4EAaGhpy6dKljI+PZ1BQEDt37qy+d968efJgIgu+/fZbWlhYcN68eTrzyHPnztHPz481a9bkqFGj2KBBAxYpUkTtUyIiIpg3b17pT8QnRQLmOcjQoUP55Zdf6lx7+vQpvby86OnpmW6yf/78eXbo0IFmZmYZOtRC/N+mTZtobm7OEydOqIOU8ePHs2DBgurhE7du3eLAgQP55ZdfSjqQLHhXnSUmJtLf35+GhoYMDw//y3tFxmkPtDdv3sx58+Zx/fr16pbyOXPm0NbWll27duXNmzd5+vRpBgYGslatWhKw/QguXbrEJk2asH79+jx58iQfP37Mbdu2sVGjRvT29maePHloZWVFRVHSPbgQf0+7jU6fPp2WlpY8evQoybQc/YqisFatWty8ebPal6xYsYIBAQGySyWLLly4QBcXF06fPl3netOmTakoCs3NzXWC5osXL2aePHlYpkwZeUCRRampqerqOPL/q2lfvnzJDh06sFGjRurOFY3jx4+zUaNGOju1QkNDJQCQAe/77jtw4ABtbGyoKIrOrooXL16ohzRrrFmzhoqiMCYm5h8v7+dm0KBBLFy4MDds2JAu5/uwYcNoY2PDFi1a0Nvbm0WLFlX/PSxcuJBGRkbSvv/G+9r32rVr6eLiwvnz5+tcX758OTt16kRXV1c2btyYZNpOxJIlS0pdZ8GgQYNobm7OJk2asEKFCvTx8dHpT8LCwli5cmW2aNFCp/+eM2cOzczMpM4z4YcffmCxYsW4b98+nV0oGoMHD6aiKCxXrhydnJzUe7Zv305FUaSuM2n16tUsXrw4Dx06pF5LSkri7du3SZK///47O3fuTG9vb9avX1+t75s3b7JRo0byfSk+ORIwz0F69OhBZ2dn9WfN07ioqCiWKVNGZzXuy5cvuWvXLrZt21ZOdP4bI0eOTLf9Kjo6mi4uLoyPj9eZ1I8cOZKmpqZqXT9+/FgNwEhQN+O0B+p37tzhixcv1GuJiYn84osvWLVqVa5bt04OPPyIgoODaW5uTldXVzVtkyaf6uLFi2ltbU1TU1Pa2dmxevXqkqbiI7p48SLr1atHX19fHjhwQL3+9u1b/vTTT5w5cyZbtWr1zsG8eLfBgwerD4NTUlL45MkT9u/fX53or1mzhqamphw/fjwdHR3p5ubGTZs2pWvPErjNOE0/vGHDBjo4OKhBcZKcMGECnZyceODAAQYGBtLc3FzdUv7kyRNu3LhRHbdInWfOn7//IiIiWKpUKTU9QmxsLPPkycOBAweqv5NHjx6xYcOGrFOnjtrm9+/fTwMDA8kz/De0+4jY2FhOmjSJc+fO5f79+0mSX3/9NcuUKcOxY8fyyZMn/O233+jv769zSDOZNhbfvn37v17+nG7Xrl20tbVVD498/fo1Hzx4wNjYWPXfwowZM9ijRw926tRJrfNXr16xX79+XL9+fbaVPSfQbt+bNm3ijz/+qObBfvToEdu0acPatWtz1qxZTElJ4YMHDxgYGMiQkBAuWLCApUuX5tWrV0mSZ86cyZb/hpxs9uzZtLGxUVN9RkRE0NDQkJUqVVIXZZHk1KlT2bFjR/X3deTIEebNm1fyaGfC06dPWatWLc6ePZtk2mK3HTt2sHPnzhw5ciSfPn1KMu0silWrVqljlLdv3/LFixfq4guRcePHj2e9evVIkqdOneKUKVPo6OhIU1NTfv/99yTT5vrPnj1T+3NNvT969Ch7Ci3EX5CA+SfofalTtm7dyrJly3L69Ok6A/LNmzfTyckp3QqMt2/f6uSEEumdOXNGZ5WhRlRUFI2MjBgfH0+Saj3euXOHxYoV45YtW3Tul2Bu1owYMYKVKlWira0t58yZw0uXLpFMOzDOx8eH7u7ujI2NleDKR7By5UoWKVKE+/btY0pKCk+dOsWBAweyRIkS6uqJN2/ecPfu3To7K6TuP55Lly6xXr16rFevHnfv3v3e+yRo/vdOnDhBd3d3enh4qA88X79+zb179/LRo0c8e/Ys7e3tOWPGDJJpuSsNDQ3p5uamBr1E5mkCtJMmTaK1tbXOGOPw4cPq+OXRo0f08fFhwYIF+eTJE53PkDQsmffnOtu7dy9dXV1ZuXJl9SC+pUuX0sLCgpUqVWL58uVZuXJlVqhQQefB5927d2XHYSZ8++23tLKyYv369dm4cWOamppy/fr1vHv3LocOHUpLS0sWKFCA5cuXZ506dXRSDcl3Z9Zt3LiRlpaWfPnyJc+fP89hw4bR1taWBQoUYOXKldX7tOtYHvBn3uDBg5k/f346ODhQURSOGTNGXQnapUsX2trasmDBgrS3t1cXbO3atYs2NjbqeF38Pe02mZSUxCFDhqiHXq9bt46mpqYMCQlhgwYNaGdnp7O6X3tR1h9//CEHNGdSfHw8vb29OWjQIC5ZsoTNmjVjzZo1WaNGDbq6urJr167p+moZo2Tcu+IfMTExVBSF7du3p729PVu1asU5c+Zw8uTJVBQlXarPlJQUiaOIT5oEzD8xe/bsYa1atXSCKZpO5OnTp+zatStr1arFMWPG8OnTp7x27Rr9/f3p7+8vnU0m/bm+1q5dq678fPXqFatUqcI6deowMTFRvefKlSu0tbXlvn37/tWyfo6WL19OS0tLLl26lJ07d6azszN79+7Ns2fPkkwLmtepU4c2Njbcs2dPNpc2Z9Fu25qB+ujRo+nr66tz37Vr19ilSxf6+fmlC2qRMmj8J1y6dIl+fn708/OTfuQD/frrr/T396e7u7u6yk1zENmCBQvo6enJhw8fkkw7nK9169bs0qWLtOssiomJ4fDhw0mmPVRWFOUvg68zZ86kn5+fzneoyLydO3eqOZw7deqknpuya9cuVq5cma6urmrQ/ODBg1y6dCkHDx7MyMhISTn0AVatWsUSJUrw4MGDJMn58+dTX1+fixcvJpkW+Hry5Am3bt3KixcvykPmLHpXgPvSpUt0dXWlo6MjixYtyi5dunDhwoX8/fffmStXLtkhkUXadX3s2DF6eHjw4MGDfPXqFcPDw2lkZMRBgwbx5cuXfPnyJa9du8bQ0FCd3Z7ffPMNvby83jlmFH/txx9/5KlTp3jz5k3evn2bly5dooODA6dNm0YybUxjbGzM0qVLMzo6Wn2fzO8z5n0Py77//ntWrFiR+fLl4/Dhw9U5ZdeuXdmtW7d/s4ifFe36jo+P5+vXr9UxeHh4OH19fTl//nz+/vvvJNNSsVSrVk0etokcRwLmn5gLFy6wZs2arF+/vk4wRTPBf/DgAb/++muWLVuWhoaGdHFxYaVKlWRlRRZo6urt27e8efMmTUxM2LRpU3X71YYNG+ju7s6qVatyz549/OWXX9igQYN0B96IjPlz21y6dKlO/tu5c+eyUqVK7Nmzp7qC4tmzZ/z666+lvjPpXf3AzJkzWbZsWd69e1fnenR0NPPnz89r1679W8X7z7t06RLr16/PypUr89SpU9ldnBxHewX+ypUr6evry+rVq/PixYsk0yaXP/zwA8uVK8fTp08zISGBgYGBOv2N9CmZ16lTJ1avXp0keePGDTo7O7Nq1apqflXNRIlMCybWr1+fffv2zZayfg5SU1OZmJhIFxcX1qpVi82bN6epqSlPnDhBMq2f37lzpxo016z+/zNp6xnz5+/N8ePHs127diTTFlQUKFBAPVvl2bNn70xFIWPwzNGuryNHjnD37t3qA4rTp09z7NixXL9+vbrb89GjR3R3d+fOnTuzobQ5158fbE6cOJE9e/Zkjx49dK5HRETQyMiIQ4YMSTdWPHr0KPv160djY2PZpZJB2u174sSJNDEx4blz59SHD8uWLWOlSpXUQyg3bNjAhg0bctq0adKXZJJ2fa1Zs4Zz5szh999/r7bje/fu8fLlyzrvqVOnjvoAWmSO9kOcH374gXXq1GG5cuXYtm1bHjt2jOT/d+enpKTw1atX9Pf3p4+Pj7RtkeNIwPwTpFmBWK9ePZ2guSZI8Pr1az5//pwTJkzgiRMnJCdoFmh31ppJ/p49e+jg4MDmzZurE6Fdu3bR19eXJiYmdHZ2TrflVmSM9hfrwoULGRISwtatW3Pu3Lk692mC5r179043IJf6zpiff/6ZXbp0YadOnXS2df7666+0trbm7Nmz1cknmTYJqlChgjzx/5edP3+eAwYMkIFjJv15kB4UFMTy5ctTURSd9CyXL19msWLFWKpUKVpbW7N8+fKS6iaLNH1vREQEq1Spol6fPHkyLSwsWLt2bZ1DyTT5+itUqCBnUHwEL168oIWFBfX19RkREaHzWkpKCnft2kV3d3ed9Cwi6zZs2MA7d+5w3Lhx7N+/P2NjY2lkZMR58+aRTGvLq1at4ujRo6W+P4B2nzBkyBCWLVuWNjY2dHNzY/369XXuff36Ne/du8fAwEBZtJJJX375pc5BtGTagZOKorBy5crpcgbPnz+fpqam7N27t85rUVFRbNKkiZyLlQVxcXEcPXq0epih9uHjDg4OXLduHZ8/f87AwEAOHTo0XV5nkXEDBgxgkSJF6OXlRXNzc9rb23PevHl8/vw5ybTd+ocPH6a/vz9dXFwkdvKBhg8fzkKFCnHevHkcMmQIGzZsyHz58nHXrl0k03aLL1myhN7e3qxYsaIs8BQ5kgTMP1HaQfO9e/eq11NTU3nnzh36+fnpbCOSL9WM0+6k58yZw+HDh6uHfhw4cIClS5dms2bNdFZ+nj9/nnfu3JEtt1mgXd9DhgxhgQIFWLVqVebOnVtdAaotLCyMlpaWan4/CbRkXHh4OI2Njdm5c2fWrFmTbm5u3LBhg/r64MGDaWZmxu+//5579uzh1atXWbduXXp7e8vgJRtJ3WfezJkzaWRkxK1bt/LKlSucO3cuvb29WbVqVbVPuXLlCiMiIiQ1xUdy6NAhGhsbqwfxkeR3333HkiVLMnfu3GzRogVr1qzJatWq0cPDQx4ufwDtHXC3b9+mm5sbnZyc6Ovry19//TXdvbt27aKlpSU7duyYHcXN0TQr9sm0g90tLCx49epVLlq0iPny5WOuXLnUYDmZtrq8bt26HDBgQDaU9vMzdepUFipUiAcPHmRycjLHjh1LRVHUVeSvX7/mokWL1HNtpF/JnGvXrqkLg7QfbE6ZMoWKonDq1KlqMFFjxowZrFOnTrrx9/t2sYj327VrFxVFYb58+dTzgjQuXLhAPz8/WlpapnuwL3OfzFu7di2LFy/OkydPqm2+c+fOrFy5Mn/88UeSaYuKatWqxQYNGkhf8oFu3bpFV1dXrl27Vr2mOf+gUKFCjIuL45MnTzh//nwOGDBAxuEix5KA+SfsXSvN79+/T29vb9ra2spquSzQHoAEBwezRIkSDAsLU097J9NWmpcuXZrNmzfnoUOH0n2GBLey5vz58+zVq5cabFmxYgVr1qzJoKCgdFub161bJwOYTIqMjKS+vr66guX27dt0cnJibGwsk5KS1PvGjh1LNzc35smTh+XKlWPVqlXlib/IMVJTU5mcnMzWrVuzV69eOq/FxsayXLlyrFGjBuPi4tK9V/qUzNm8eTPHjRvHI0eO8Nq1a7x8+TJLly6tnvWhsW/fPg4ePJgBAQHs0KEDIyIiZOfbB9Duh7du3aquYn78+DHd3Nzo4+PDLVu2pAuoxMXFSRvPpLlz59LZ2ZlPnz7l3bt32atXL27evFl9vX///lQUhStWrOCJEyd4+vRp1q1blxUrVpTdE1n05/pq164dIyMjSZLr16+nsbGxupPi5cuXJNP+HcyaNUsCLpmkPU8MCwujh4cHd+zYoV4bPXo09fT0OHPmzHRBc+3DJqWNZ9y7xtETJ06koigcNWpUurZ78eJFbtq0iUuWLJHvzQ80Z84curm5MTExUad/btasGStWrKjed+TIEVkA9xFcvHiRuXPn1vnOJNPiV9WqVVN3kWufYyNjFJETScD8E6cJmvv7+/Onn36ir68vnZyc1EGQdPQZo8mjpbFw4UIWK1aMhw8fVq+lpqbqHJxlb29PX1/fdwZeROasWbOGVlZWdHNz44MHD9Try5Yto4+PD5s0aaIe9qlNvlgzJjo6moqiMCoqSue6m5sbvby8WK5cOTZo0EDN5Xf16lUeOnSIv/32mwwaxSdP00a1J+2dO3fmF198ka7dDhw4kIqi0NbWlleuXPlXy/k5SUhIYL169ejs7Ew7Ozvmz5+ffn5+VBSFgYGBPHjwII8fP/6XnyH9d+b9OU2Fk5MTZ82apY5Nbt26xUqVKtHX15cbNmzgmzdvWKNGDY4YMUJ9n9R7xsybN4+KojAmJoYbN26koigsVaoUf/vtN537evToQSsrKxoZGdHd3Z0+Pj6yMjGLtIOJp0+f5ps3b+ju7s5Fixbxl19+oZGRkRpkefv2LSdNmsTY2Fidz5A6z7z4+HhevnyZDg4ObNiwoU4O+JCQEOrr63P27NnpVpBLoDzrli5dqrNDfPTo0dTX1+fChQv/8n3SvjNP069MnDiR9vb26nXNA7fLly8zb968Omlutd8n/p52X6D586tXr1irVi32798/XXqyatWqSX548dmQgHkOcOnSJQYEBFBRFAmWZ0GrVq3UAbemk//mm2/41VdfkSTPnTvHsLAwVqpUiba2tly9ejVJcvv27WzevLl8oX4EsbGx9Pf3p5GRUbrV5MuXL2edOnXo7e0tB09m0YYNG6goCocOHapOeIKCgtSc5RMnTqSlpSU9PT3f+X5p4+JTtXz5cn711Vc8d+6czmQ+NDSUDg4O3Lhxo84OigULFtDf35/fffedTDw/kGaMcfPmTW7ZsoXR0dG0s7NTH0gYGxvT3d2dvr6+/Oabb+QA249o1KhRLFSoEPft26eu/NSMX27evMnq1auzbNmydHBwYLly5XQOXBV/b+XKlVQURQ2gPHv2jF9++SUVRWF0dHS6+0+fPs19+/bx7Nmz8pA5i7QDLoMGDaKvry+vXbvGr7/+mrVr16aJiYlO6pt79+6xfv36nDNnTnYUN0eLiYnhunXrSKbldO7SpQvJtJ2ezs7OrF+/vk7QfMyYMVQURZ3/iA/z/PlzFi5cmJ6enjoP4EaOHEl9fX0uWrQo+wr3GXjfnOX27ds0MTFh9+7dda4fPnyYZcqUkQVwWaRd30lJSTrB8TFjxrBcuXIMDw9XFye+ePGC1atX56RJk/71sgrxT5CAeQ4RFxfHr7/+WrYjZsHQoUPVyaTm/6dMmcKiRYty4MCBrFixIoOCghgSEsLu3buzQIECfPjwoc5nSEAx495XV9u3b6eXlxcrVaqULm95ZGQkv/76a6nnTEpOTlYnoevWraOiKBwyZAgbNWpEFxcX/v777+q9UVFROgECIT51T58+pa2tLYsUKUIXFxe2b9+eCxYsUF9v0qQJ7e3tuWLFCt66dYtPnz5lo0aNGBISIodmfQTvWl3Yt29fDhgwgPfv3+eJEycYHh7Odu3asW3btlLXWbR06VI+efJE/fnatWusUqUKf/nlF5JpgcNDhw5xwIABXLVqlXotKiqK8+bNk3FhJs2fP5+KolBPT09nl8Tz58/ZqFEjmpubq7sP37fCVsYqWXfu3DlWr15dHYscPHhQXb1/8eJFkuTdu3cZEBBADw8P6Vcy6fnz5+zWrRsNDQ3ZpEkT5suXTydP//uC5gsWLJA+JIve1U9o0iLWqlWLBw8eVK+PHDmSuXPnlgdBWaTd98bGxnLKlCmMiopS63j58uUsUKAA27Zty2PHjvHo0aNs0KABPT09pd/OAu06Gz9+PH19fWlnZ8dOnTqp35+9e/dm2bJlWadOHQYHB9PT05Nly5aV/kR8NiRgngNJB5Qxf/5inDt3LkNDQ/nq1Steu3aNI0aMYPny5TljxgyeP3+eZNrhLN7e3jppQ0TGadf5jh07uGnTJq5fv169tnPnTvr7+9Pd3T3dSvN3fYZ4P+3VtidPniSZlvpGURTmyZOHx44dI/n/gfyGDRvo6OjIS5cu/fuFFSIL3r59y6FDhzIsLIzHjh3j5MmTaWJiwqZNm3L27NlMTk5m48aN6eHhQRMTEzo5ObFMmTKSW/gfFBYWxpIlS/Lx48fvfF2CW5kTHh7OevXq6Xzv/fHHH7SxseHEiRN55MgRtm3blhUqVKC7uzsVReHSpUvTfY7Ue8ZERETQwMCAs2bNYrdu3WhmZsZdu3apr7948YIBAQEsUaKEzuG24uP44Ycf2KhRIzZt2pQvXrxQr2/bto1Fixalm5sbHRwc6OHhwcqVK0vqmyz6448/WKZMGSqKwhkzZpBMmztqvhvPnz9PFxcXBgYGqg/mNGSOmXVPnz4l+f+xx507d+jg4EAvLy+dleb9+vWjl5eXjFE+QHBwMIsVK0Z3d3eWLVuWhQoVUg/23LBhA0uVKsXixYvTzs6OXl5eclbTBxoxYgQLFSrE8ePHc8aMGXRycqKnp6eav3zx4sXs3LkzAwIC2KdPH+m7xWdFAubis6UZiGi+HOvXr8/SpUszKipKXWmufchNcnIy/fz82KBBAxnEfCDNgaq2trbMly8fa9eura7Y2rZtm7py6O9y4Ip306QLevXqFb/++ms6OjqqKxQ1uViDg4N5//599T0NGjRggwYNZLAocpTNmzfT2NhYTffx6tUrjho1ioqisFatWvzhhx84Z84crly5kkuXLlUH5zJI//hSU1O5a9culihRQu1vNPUsB8NlnaYODxw4oJ4zMWzYMJYqVYq5cuViv379uHHjRpJkw4YN2bdv32wra062evVqKorCn376iWTazs127dq9M2hev359WllZcf/+/dlV3M/S4sWLqSgKixUrxgsXLpD8/1j97NmzjI6O5rhx4xgTEyMHIGaS9tju4cOHbNOmDZs2bUoTExOuXbtWvUcz/7lw4QKLFi3KAQMGZEt5PzdTpkxhzZo1efXqVZ3rd+/epZWVFb29vXUOzNY+VFVkTkxMDAsXLswDBw4wNTWV165dY0hICPX09Lh8+XKSafnLjx8/zjNnzkgarQ909epVOjo6qt+d5P93AXl6evLevXvqde06lvoWnwsJmIvPkvbAUfvgt6+++ooODg5cuHChGix//vw5165dSx8fH1aoUEGeQn+g+fPns0iRIjx27Bhv377Nq1evsly5cvTw8FC3227evJnVqlVT8yqKzJk3bx5r1KhBFxcXmpmZqW1cM8Fcu3YtFUXhwIED+eDBA9avX58ODg7StkWO1Lt3b/bq1Uv92dnZmY0bN+bAgQMZGBhIRVHUSRIpwfJ/UlJSEkuWLMlNmzZld1FyPO2HDTt27GC+fPk4fvx4JiYm8uXLl4yLi9NJpZCSksIaNWpIXtAsev78OXfs2KFz7cKFC+8NmletWpUNGzb8t4v52XjfOCMmJoaKorBPnz589OjRX36G9OUZo13Xv/zyC8+dO8ekpCQ+ePCAvXr1orGxsRo013j58iXv378vdfyRnD59mnnz5mWTJk3UoLnm97J69Wrq6eml210rwfK/p11HmvrUPJzQFh8fzwEDBrBixYq8efNmus+ReU/W3bhxg9bW1upqcs1c8sGDByxYsCCnTZuW7j3StsXnRA9CfGZSU1Ohp5fWtL/77ju0bt0aW7ZsAQBERUWhSpUqmDhxIlavXo2XL1/i8ePHOHPmDOzt7XH06FEYGhri7du36meI9/v1118RHx8PACAJAIiLi0PNmjVRqVIlFCtWDKVLl8bu3btx//59hISEAAD8/Pwwc+ZMhIeHZ1vZc7IePXrA0tIS586dQ7Vq1VCwYEH1tdTUVAQFBWHt2rWYOXMmrK2tcfPmTZw9e1batsiRKlasiFOnTuHJkyeoVKkSChYsiKioKEyZMgURERGIjo5G8+bN1fv19fWzsbSfvxcvXuDGjRvZXYwcjaTaThVFgY+PD/r06YPw8HCEhobi2bNncHR0hKurK168eIETJ04gMDAQiYmJ6N+/fzaXPmchiZSUFBgZGcHHxwcAkJKSAgAoU6YMRowYgQYNGiAoKAi7du0CAOTLlw+7d+/GunXrsqvYOZr2OPz8+fP47bffcOfOHbx+/RpNmjRBVFQUQkNDMXnyZDx+/Fh9n2YcqSF9+d8jqdb1sGHD0LVrV5w8eRLJyckoWrQogoOD0bZtW3Tu3BmrV68GADRu3BgjRoyAubk59PX11X8PImNSU1N1fk5JSUG5cuVw+PBhbN++Hf3798e1a9fU3wtJdOjQAaVLl4aTk5P6PkVR/tVy50Tada2pz4IFC+LSpUu4efOm+pqpqSlq166NGzdu4MWLF+k+R+Y9GXPjxg2cPHkSz549U6/lzZsXKSkp+O233wCk9ctv375F0aJFUaVKFTx8+DDd50jbFp+V7IzWC/FPGjZsGIsWLcrY2FidVeYk2bZtWzo5OTEqKopv3rxhYmKiHBKXSeHh4cyfPz/nzZunk7evdevWrFOnjnqf5tTsNWvWsESJErx+/brO58hT/4zRtM83b97w1atXnDFjBkePHk0fHx+2bt1arVfNdlsy7SBQDw8PdTWAbI8TOVWVKlWoKApr1qz53vzZ0r7/HUuXLpW6/gDaK6/WrFnD6Oho9echQ4bQysqKEydOVM9SWbFiBRs1asTatWtLXtBM0h5fJCQk6PysqUuSvHjxItu3b88iRYqky+ksY5TM0W7fgwcPpoODA/Pnz88KFSqwefPmTEhIIEn++OOP6kHlDx8+zK7ifja+++47mpubc9++fTr54cm0FC1ff/01FUVhuXLlaG9vr9P+RcZp9wfR0dEcO3Yshw0bxkOHDpFMO9TW2NiYjRs35q+//sp79+6xYcOGDA0NVd8n/XfGrF+/nu3atWOLFi04depU9fqhQ4fo6urKkJAQ3rlzR71+7tw5Ojs76+zMEhkXFRVFZ2dnmpub09bWltu3b1dfi4yMpIGBASMjI9VrycnJdHV1lV1v4rMnAXPxWYqLi6OzszM3bNigc117gNiuXTuamJiouUFJ2UKUWT179qSdnR3nzp2rBrE2b97MvHnzcv78+Tr3rly5kuXLl1dz34qM0x6gv3z5Uue1OXPm0MvLi61bt+aNGzfU63v37tW5TwJcIifS9MlLliyhi4sLjx49qnNdZB/pUzJPuy8/efIknZyc6OvrqxOkHTp0KK2trTlx4kQ+e/aMjx8/5o4dOySn8wf44YcfWLVqVTZo0IBTpkxRr/85aB4YGEh/f//sKOJnQ9PGp0+fTjMzM27ZsoWnT5/mnDlzWK1aNXp7e6uHli9fvpyKougEE0XmPX78mF5eXoyIiCCZdtjkvn372LNnT86ZM0cdd2/bto3z58+XvuQjCA4OZsmSJdm4cWO2adNG50DmuLg4li1blpaWlrSwsGClSpXkAUUmhYeH09jYmN27d2dQUBBLly7NZcuWqa+PHj2ajo6O7Nu3L7dv384zZ86wbt26rFGjhjzkzIKwsDDmzp2bYWFhPHPmDGvWrEl3d3f19cTERI4YMYKKorB169bs06cPa9euTWdnZ+lHxGdPAubis7R7926ampry8uXLJHWDK9oBx9GjR8uT/ixISkpS/9ytWzeWLVuWoaGhfPr0KV++fMng4GCWKlWKc+bM4fPnz3n79m3Wr1+fAQEBEuj6AOPHj2etWrXYsGFDzp07V70eGhrKmjVrskmTJty/f786aJS6Fp+L27dvs3jx4hw/fnx2F0WIDzZ06FB27NiRLi4uzJ07N728vHQO1Bo2bBhtbGw4YsQINbhIymrnjNKup9DQUJqZmfGHH35gixYt6OLiwq5du6qvaweybt68KXWcRZqHmWTaGLF58+YcPXq0ei05OZmbNm2im5sbhw8frtbz1q1bJeDyAVJTU3n//n06Oztz4sSJXLt2LVu3bs0aNWqwQoUKdHV15ahRo9LNdWTuk3madhoTE8MSJUrw8OHDJMkNGzZQURSdgO7du3e5detWxsbGygOKTNKsZo6JiSGZliu7Ro0aXLlypXr+GEnOmDGDtWvXpqIoLF++vM6OWunHM27BggXMlSuXzgLCLVu2sEmTJvz555+5c+dO9byJ9evXMyAggEFBQezRo4fapqU/EZ8zCZiLHE87KKj586lTp2hjY8P169err2k686ioKK5Zs0bnM6Sjzzjt+l68eDEnTJjA3Llz09zcnPPmzePr1695584djhw5knnz5qWFhQXt7e3p5uYmA5lM0q6nSZMmsVChQhw8eDBbtmxJY2NjDhs2TH09MjKSPj4+tLCwoJeXl05qFiE+B7NmzWKhQoV47ty57C6KEFkWGhpKY2NjHjx4kLdv3+bevXtZqVIl+vn56UxY+/TpwyZNmsiDzw+wY8cOTpw4kT///DPJtLQsYWFhtLOzY+fOndX7/vx9KWOUzAkLC6OiKLxw4YJ6zdfXly1btkx3b5cuXVinTp10dSzBxIx5X9scPnw4LSwsmD9/fg4ZMkQ94LZZs2bs3bv3v1nEz86WLVt0+uHZs2ezY8eOJNMO9DQyMmJ4eDhJ8unTp+lST5Iyz8yo2NhYKorCWbNm6VyvWLEiK1euzJIlS9LX15fHjh0jmVbfJ06c4NmzZ9V/G9KXZNzvv//OEiVKsHr16jrXfXx8WKxYMVpaWrJ48eKsUqUKr127RpLpdktIfYvPnQTMRY7254GjZvV4QkICK1euzDp16uicSJ6cnEw/Pz9269btXy3n5ygkJISmpqZctmwZo6Ki2KhRIxYpUoTz5s1TV6BfuXKFq1ev5q+//iorLD7A0aNHGRoayl9//ZVk2gAxNDSU+vr6OkHzmzdv8tSpUzJoFJ+lK1eu8KuvvpJglsjROnfuzKCgIJ1rBw4cYKlSpVijRg2doLnme1OC5pm3d+9eWlpasnDhwjopyhISEhgeHk4HBwedleYia8LCwmhgYMB169ap11JTUxkSEsKqVavyyJEjOn32nDlz6OnpqbNSVGSMdj3GxsZywYIFnD59upoX/uLFizoPLci0BxdDhgz5V8v5OXn8+DFtbGzo6Oio9sMhISFs1KgRV69ezQIFCujs+Pzxxx/Zo0cPnZ1BIuNiY2NpYmLC/v378/fffydJBgUFsVSpUoyIiOCCBQtoZ2fHKlWqvDPNjYwPMyc+Pp4RERG0sLBQH6y1aNGCjo6OPHHiBJ89e8aFCxeyRIkSDAkJYUpKis7cUsYm4r9AAuYix9L+UpwyZQpbt27NMmXKcNKkSbx58yZv3LhBS0tL+vj4cNiwYQwPD2fNmjVZrlw5CSR+gNTUVP7xxx9qGhZtHTp0oImJCefNm8c//vgj3XtlhUXm7d69m4qisFChQjqT/sTERIaGhtLQ0JAjRoxI9z6pa/E5ksOZRU6lGbP07t2bfn5+JNPas6YtL1iwgPny5WPjxo25detW9X0yIc2amzdvctSoUTQzM2Pfvn11XktISGBERASNjY0lzdMHWLhwIfX19XUe8pBph0w+evSI9vb29PPz486dO/nq1SsmJCSwdu3abNOmTTaV+PMwYMAAmpub09XVlcWLF6e1tTVjYmL46tUrkmmLKo4cOcL69evTxcVF5jwfIDU1lfv376eLiwtdXV2ZmprKw4cP08nJiXny5OG0adPUe58/f84GDRqwT58+0m9/gNWrV9PS0pLffPMN69aty/Lly+us2t+4cSMVRdE5lFJknmbskZCQwEWLFrFo0aI0NzdnxYoVef/+ffW+lJQUlitXTnaqiP8sPQiRQ+nppTXfYcOGYfLkyahSpQoGDBiAH374AX379oW1tTV27dqFUqVKYePGjfjxxx9haWmJY8eOwcDAACkpKdn8X5AzKYoCQ0NDkIS+vj4AICkpCQCwaNEilClTBjNnzsTChQuRmJio817N/SLjbGxsEBISglevXuG3335Tr+fPnx/t27fHrFmzMG7cOERGRuq8T+pafI4URQEg7Vt8+lJTU3V+1oxZatWqhV9//RWrVq2CoihqW86VKxd8fHxw+/ZtLF++XH2fps2L9/tzXQOAlZUVevXqhb59+2Ljxo0ICQlRXzM2NkaLFi2wZMkSfPvtt/9mUT8b+/fvR+fOnTFgwAAEBASo15s3b46pU6eicOHC2LZtG+7du4dvvvkGDg4OqFOnDh49eoRFixYBAEhmV/FzrOjoaCxZsgRbtmzB7t27cfv2bVSpUgXBwcHYv38/AGDfvn3o1asXUlJScPz4cZnzfABFUVCtWjVERETg1atX8PDwQJUqVdC0aVMUKFAAr169wtmzZ3Hw4EE0b94ct2/fxvTp06EoirTvTNLUV7NmzTB16lRER0fj0KFDmDZtGkqWLKnelzt3bpQpUwbm5ubZVdQcbffu3Zg6dSp69+6Nly9fwtjYGM2aNcPEiRORL18+ODg4qHWbmpqKxMREGBsbw8bGJnsLLkQ2USi9ucjBjh07hrZt22LhwoXw8PDA0aNHUa1aNSxcuBBfffWVel9ycjKSkpJQoEABAMDbt29hYGCQXcXOUVJTU9WJvra6devixYsX6gA9OTkZhoaGaNWqFfbs2QMfHx8sXbpUJvuZ8L66jo+Px6RJkzB9+nRMnz4dPXv2VF9LTEzE1q1bERgYKG1aCCE+Adp9+fbt2xEfH4/cuXOjbt26yJ07NwYPHowZM2YgPDwcXl5eKFiwIDp06IDAwECYm5ujcePGOHPmDMqWLZvN/yWfPu26XrJkCa5evYonT56gXbt2qFKlCuLj4zFr1iysWLECLVu2xJgxY9J9RkpKijyEy6SXL1+iXr16ANIWrvj7+6NVq1Y4fvw4tm7dqga4Hj9+jMOHD+PcuXMoUqQI2rRpAwMDAxmHZ9GUKVOwYcMGbN26FYqiqHXo5+eHR48e4dixYwCA3377De7u7tDT05O6zqTDhw/j8ePH8Pf3V+vu7du3OH78OFq1agVLS0vs2bMHo0aNwoYNG3Dy5Em4u7vD2NgYGzduhKGhofQpGfS+eQ8ArF+/Hr1790aTJk3Qs2dPODs7AwAaNmyI5ORkbNy48b3vFe8WFRWFCRMmoEmTJrC1tUXnzp3V1+Lj47F+/XoMGTIEgYGBmD9/PgAgICAA9+/fx+HDh6UfEf9N2bm8XYgP9dtvv7FSpUokyZUrV9LIyEjNJff8+XNu3bqViYmJOu+RbXIZp5325sSJE7x8+TLv3r1Lkjx79iyLFSvGJk2a6NzbqlUr7tu3T/1Z6jtjtOs6NDSUffv2Zb169bh69Wo+ePCAr1694ogRI1igQAHOmzfvnZ8h226FEOLTERwcTGtra1pbW9PGxoY2NjbqobUhISHMly+f+rqzszOTkpJ44sQJ2tnZvfPgOPF+AwcOZNGiRenr60t3d3fmzp2bEyZM4IsXL/jo0SOOGTOGZcuWZb9+/bK7qDmeZqyRmJjI2rVrs1q1avTw8GDZsmV579499b735ROWlFqZpxlLDxs2jPb29up1zdlNR48eZeHChXny5Emd90lO58zZsWMHFUWhoiisVq0aO3TowHXr1vHGjRskyUOHDtHV1ZU1atQgmXYA4q5du3jjxg05PyiTtNtmTEwMw8LCOG/ePD558kRt79HR0bSwsGCfPn0YFxfHwMBA2tvbq/nLpX1n3JIlS5g3b14uX76cjx8/Vq+PGDGCcXFxJNPSOS1atIjFihVj9+7d2aRJEzo4OKj1LX23+C+SgLnIMe7du8fTp09zyZIlPHPmDJ88ecJTp06xSJEijIiIoImJiU5O7W3btjEoKIgXL17MxlJ/HgYNGsSSJUvS1NSUTZs25c8//0yS3LBhA0uUKMEyZcqwQYMGrFSpEu3t7dUvVBnIZN6gQYNYpEgRjh07ll26dGHp0qXZqVMnJicn8+7duxw5ciRNTU05efLk7C6qEEKI91i4cCHNzMx4+PBh3r17l+fOnaO/vz8tLCx48+ZNkmkP/X/++WfGxMSo35sDBw6kq6vrO88BEe+2adMmmpub88SJE+q4Y/z48SxYsKA6Lrx16xYHDhzIL7/8Uh7kZ9G76i0xMZH+/v40NDRkeHj4X94rMu594+fLly+zSJEi/Oabb3Su7927lw4ODrxy5cq/ULrP15UrV+jh4cHKlSvTz8+Pffv2pampKW1tbdmwYUPOmDGDixcvpoWFBevUqZOuncu8J2O0623w4ME0Nzdn7dq1aWZmRj8/P27ZskUnaF6yZEkaGRmxbNmyavBWHkxk3IULF+ji4sLp06frXG/atCkVRaG5ublO0Hzx4sXMkycPy5QpI/Ut/vMkYC5yhLVr1zIgIIDFihWjsbEx8+bNy4YNG/LgwYPs06cPFUXh6NGj1fuTkpLYoEEDNm3aVAYvWaA9kNm+fTtLly7NXbt2cfHixWzRogUrV67MmJgYkuSDBw84cOBA9unThwMGDFC/UOUpdObt3LmTdnZ2PHr0KMm0ujcwMODSpUvVex4/fsy+ffu+c6AuhBDi0zB06FB++eWXOteePn1KLy8venp6ppt8nj9/nh06dKCZmVm6VaLi/0aOHKmu0teIjo6mi4sL4+PjdepV84BZs+r58ePH6vemfH9mjvZY+s6dO3zx4oV6LTExkV988QWrVq3KdevWqb8DqeOs0a7rzZs3c968eVy/fj1Pnz5NkpwzZw5tbW3ZtWtX3rx5k6dPn2ZgYCBr1aolc56P4NKlS2zSpAnr16/PkydP8vHjx9y2bRsbNWpEb29v5smTh1ZWVlQUJd2DC/H3tNvo9OnTaWlpqc57VqxYQUVRWKtWLW7evFntQ1asWMGAgAC1b5HgbcZo6nrDhg10cHBQg+IkOWHCBDo5OfHAgQMMDAykubk5z58/T5J88uQJN27cqM7lpb7Ff5kEzMUnLyIiggULFuSUKVO4bds2xsfH87vvvqOjoyPLlCnDUaNGsVWrVixZsiSjoqI4ffp01q1bV+cptAwgsyYmJoY9evTg+PHj1WvHjh3jV199RTc3N0ZHR7/zffLF+vfGjBmTbtL/008/0cPDg2RaAKBAgQI6KYb27NlDUib9QgjxqevRowednZ3VnzUTz6ioKJYpU0YndcXLly+5a9cutm3bVg2KifTOnDmjEzTRiIqKopGREePj40mSr169IpkW2C1WrBi3bNmic798b2bdiBEjWKlSJdra2nLOnDm8dOkSybQxio+PD93d3RkbGyvjwI8gODiY5ubmdHV1VdM2rVixgiS5ePFiWltb09TUlHZ2dqxevbrMeT6iixcvsl69evT19eWBAwfU62/fvuVPP/3EmTNnslWrVmqdi783ePBg9WFwSkoKnzx5wv79+3P+/PkkyTVr1tDU1JTjx4+no6Mj3dzcuGnTpnTtWfqWjHv27BlJctKkSbS2tla/G0ny8OHD6k62R48e0cfHhwULFuSTJ090PkMWwIn/OgmYi09aREQEc+XKxbVr16Z7LTo6mpUrV6a3tzejo6PZq1cvWllZ0cfHh507d5an0B/o6tWr9Pb2pqmpKYODg3VeO3bsGNu3b093d3cuWLAgm0qYc23bto2tW7dO1zYXLFjAatWqccuWLTQ2NuacOXPU19avX89evXqpOeRJmfQLIUR2e1/qlK1bt7Js2bKcPn26Tl+/efNmOjk5qWlZNN6+faszmRW6/vx9t3btWjWQ9erVK1apUoV16tTRObfmypUrtLW15b59+/7Vsn6uli9fTktLSy5dupSdO3ems7Mze/fuzbNnz5JMC5rXqVOHNjY26gN+kTUrV65kkSJF1DOBTp06xYEDB7JEiRJcs2YNybT82bt379ZJRSRzno/n0qVLrFevHuvVq8fdu3e/9z4Jmv+9EydO0N3dnR4eHupiodevX3Pv3r189OgRz549S3t7e86YMYNk2gIiQ0NDurm5cf/+/dlZ9BwrJiaGw4cPJ5n2UFlRlL/cvTZz5kz6+fmlO/tNiP86OVpYfLJ27dqF7t27Y/jw4QgKCgLTHvDg7du3AICWLVuiXbt2OHXqFAwNDREaGopjx45hx44diIyMVE81lxOds6Z06dIYNWoUqlSpgnXr1mHbtm3qa5UqVcI333wDc3Nz7N+/PxtLmTN98cUXiIqKgoGBAdavX4+DBw8CSGvT9+/fR7169TBnzhz07t0bAPD69WuEh4cjISEBxYoVUz9HUZRsKb8QQghg7969aNasGfbs2aNeIwkAqFKlCqpXr47169fjhx9+QEJCAn7//XfMmjULNjY2sLS01PksfX195MmT518tf06iqdeUlBTcunULnTp1wtSpU3Hs2DHkyZMHISEhePbsGb744gvs3bsXv/76K/r164fChQujWrVq2Vz6nCk1NTXdzwMHDkSbNm0QGRmJPn364ODBgwgNDcX58+dhZGSEmJgYBAYGonr16tlU6pxH07aB/9d5XFwcXF1dUaNGDejp6aF8+fLo3bs3AgICEBkZifj4eBgaGsLb2xuurq7Q09NDSkqKzHk+Int7e8yePRuKomD8+PHvne8YGhr+yyXLeVxdXTF27FiYmpqiY8eOOHv2LHLlygV3d3cULlwYhw4dgrm5Ob788ksAwPPnz9GsWTNUrFgRVatWzebS50wbNmzAzp07AQC1atWCk5MTunfvjhs3bgAA3rx5o977+vVrbNmyBQ4ODsifP3+2lFeIT5UEzMUny8LCAp6enjh+/Dj27t0LRVGgKAoMDAzUAWXfvn1hZWWlBnNNTU3V95OUgWMGaU+KtAfuX3zxBQYNGoQyZcpg0qRJ2LFjh/paxYoVMWXKFMyfP/9fLWtONmTIEAwZMgRA2gD77NmzGDhwIGbPno0jR44gf/78mDZtGiwsLLBixQrs2LEDq1evRqNGjXDz5k0sXrwYiqLo/I6EEEJkj6JFi4IkJk2apAZTFEVBSkoKTExM8P3336NcuXJYtWoVihQpgoYNG+LBgwdYv349FEVJF5AU75aamgo9vbQpS0pKCqysrPDzzz/jzJkzmDhxIs6ePYv69etj0qRJMDY2RmBgIAYMGICkpCTs3bsX+vr6SElJyeb/ipyFpFrnixYtwujRo7Fx40bkzp1bvadnz57o0qULDh06hLlz5+LUqVMoUKAAZs2aJXWeCdpjOk2dFyxYEHfv3sW9e/fU10qVKoU6depg7969ePr0abrP0dfX/8fL+l9jb2+vtud+/frh9OnT2V2kHCc5ORkAULduXXTo0AEmJibo3r07Ll26hFy5coEkHjx4gISEBNy/fx/Pnj1DdHQ03N3dMX/+fOlLMklTV9WqVVPr3traGh07dsTt27fRqVMn3LhxA7ly5QIAXLp0CY0aNcLt27cxdepUAJB5phBaFMq/CPEJu3z5Mvr27QuSGDFiBDw9PQGkdeSKouDZs2dwc3PDV199hZEjR2ZzaXMm7YnowoULcfjwYeTKlQvVqlVTn/Rv3rwZoaGhePPmDYYOHQofH5/3foZ4t4SEBPTt2xeXLl1C48aNMXjwYADAihUrMHPmTNjZ2WHQoEEoX748du/ejQEDBuCPP/6Aubk5SpUqhaVLl8LQ0BApKSkyKRJCiE+E9jhl5MiRqFGjBoC0IIGhoSHevHmDN2/eIDQ0FPXq1UO5cuWgr68vO+AySHt8ERoainv37uHbb7+FiYkJDh48iLZt26JSpUoYOXIkypcvDyBtda6JiQmKFSsGPT09qetM0q7zoUOHIjQ0FM7Ozjh58iQcHBywbNkylCtXTr0/PDwc33//Pb755hsEBwerY3Tx9zZs2ID169cjNTUVHh4e6NKlCwBgy5Yt6Nq1K7799lu0bdtWXRB07NgxdO7cGatXr4a9vX02lvy/JS4uDpGRkZg8ebLMdzJBuy8YP348jh49iitXruDMmTOoVq0aIiMj4ezsjCtXrsDLywt58+ZFSkoKTE1NcfToUVm9/wEOHz4MX19fbN++HZUrVwYAjB07FgsWLMD9+/fRqFEjPHjwAK9fv4aiKNi9e7fMM4V4BwmYi0/euyajmi/gkydPon///hg2bBh8fX1lkP4BBg8ejMWLF6NZs2Z48OABrly5ggYNGuD7778HAPzyyy+YN28ebt26hcjISFSqVCmbS5zzPHz4EOPGjcPJkydRp04d9SFPdHQ0pk6dijJlyiA4OBiurq4AgGvXrqFgwYIwNTWFoigy6RdCiE/QXz3cv3fvHjp37gxra2uEh4cDgExIM0h7TPftt99i+fLlGDVqFHx9fVG6dGkAaWlxOnToADc3NwQHB8Pd3V3nM+SBftbFxcVhzpw56NixIypXrozo6GiEhYWhUKFCGDNmDFxcXNR7Y2NjERgYKO06EyIiIvDtt9+iefPmuHLlChITEzFmzBjUr18fQNquxPnz52PAgAHw9vaGhYUFevbsiaSkJOzcuVPadTaRPiXzZs2aheHDh2PdunUoVaoUtmzZgujoaLx+/Rrz589HuXLlcPXqVezYsQN6enpo3769pFbNpF9++QXHjx9H3bp1UahQIaSkpKBevXpYunQpPDw81Pv279+v7tAqWrQoqlevjk6dOsmDfCHe599JlS7Eh7l06RL9/PxYr1499SCh5ORkBgQEsEGDBnIi/AdasGAB7ezsePjwYZLksmXLmCtXLlpbW/Obb75R71u3bh2Dg4OlvjNJ+4Txbdu2sUWLFrSzs+PUqVPV6ytWrGDlypXZrl07Hjp0KN1nSJ0LIcSnS3ucojlk8v79+/T29qatra0cDJcJfz78dOHChSxWrJg6RiHTDgFNSEggSR48eJD29vb09fVlXFzcv1rWz9WaNWtoZWVFNzc3PnjwQL2+bNky+vj4sEmTJuphn9q0xzvi/SIjI6mvr8+YmBiS5O3bt+nk5MTY2FgmJSWp940dO5Zubm7MkycPy5Urx6pVq6p9iYwLxacuNTWVycnJbN26NXv16qXzWmxsLMuVK8caNWq8s9+WviRjNN+F9erVo7OzM+3s7Jg/f376+flRURQGBgby4MGDPH78+F9+jtS3EO8mK8xFjqFZwaWnp4dhw4Zh2rRpuHDhAk6ePAlDQ0N54v8Bxo0bh6SkJIwdOxbr169Hx44dMXToUDx+/Bjh4eHo3bu3utJcQ+o78wYOHIhTp05BT08PJ0+eRL58+dCtWzcMGzYMALBy5UpMnz4dhQsXVlecCyGEyBk04xRFUdCzZ0/Mnj0bt2/fVg8nl9Vbf69169Zo1aoVGjVqpK4w79evH+Lj4xEVFYXz589j7969iIiIQEJCAiZMmIBmzZphx44dCAsLQ3R0tIxNPoL169cjPDwce/fuxcGDB3VWk69YsQILFy7EmzdvsHjxYpQqVSobS5rzrFy5Eq1bt8bixYvx1VdfqdcrV66MfPny4enTpyhZsiQiIiJQvHhxXLt2DX/88QdIokqVKpJmSHzSNPNDau0Q6tKlC65fv45ffvlFp90GBwdj2rRpKF26NH799VfY2tpmV7FzPE2fcOvWLVy4cAFPnjzBiBEjcPXqVZQuXRqPHj2Co6MjTExM4OzsjE6dOqlpzIQQ7yfftCLH0By80r9/f/j4+KB06dI4c+aMTEIzacuWLdi6dSuePXsGHx8ftGrVCkOHDsWNGzdw9+5djBgxAsOGDUNwcDBOnjyJyMhITJ8+HaampggODlY/RyakmbNq1SosXLgQW7ZsQbly5fDs2TMMGjQI69atg76+PgYPHoyWLVvi5cuX2L9/v+SmFEKIHEYzTunXrx8aNWoER0dHCZZnUqlSpeDv7w8gLQ98rly5YGVlhRUrViA4OBg7duxAqVKlEBgYiPv376NTp06oWbMmateujdq1awOQB/qZ9a76atSoEQoUKIDExES0b98eixcvVvOWt27dGi9fvsSpU6dQsmTJ7ChyjmZkZAQAuHDhAp4/f44CBQqgadOmePToEb799lu8fPkSs2fPRosWLbB3716ULl1aTUEEpP2+pC8Rn6IVK1bgl19+weDBg2FlZYUCBQoAACpVqoS9e/diy5Yt+OKLL9TDg52dneHn5wcPDw/Y2NhkY8lzPk06LCsrK1hZWQEADhw4AAMDAwwaNAj37t3D4cOHsW/fPjx+/Bhly5bNzuIKkWPICnOR41y4cAFz587FtGnTJL9ZJs2fPx/Dhg2Dp6cnbty4gTNnziAsLAydO3cGAGzfvh29evXC9u3bYWlpiaNHj2LSpEkICgpC8+bNJTflB5g0aRKWL1+Oo0ePqu319u3b6NmzJ44dO4Zvv/0W/fv313mPTPqFECLnkXFK5v35+27evHkgiU6dOuHevXtYuHAhfvrpJ3Tq1Al169aFk5MTdu/ejVGjRmH16tUoWrRoNpY+59Ku9507dyIpKQnJyclo2LAhAGDXrl2YNGkSHj9+jAULFuisNH/XZ4j3e/v2LfT19aEoCmJjYxEUFITBgwcjLi4OV69exc8//6wGDX/88Ud06NABe/fuVQ8SFuJTlpCQADc3Nzx79gzm5uZwc3ODt7c3OnXqBAAICgrC2bNn8d1338HT0xMFChRA+/bt4erqipCQECiKIud7fGTh4eEYP348jh8/DjMzs3SvS30L8fdk9C5yHEdHR8yaNQsAZBKaCZGRkejTpw+WL1+Opk2b4uzZs/D398eyZcvQqlUr5M+fH0ZGRnj58iWio6PRunVrjBo1CsWLF0fLli1lIJNFmolk0aJFkZKSgtu3b8PGxgapqamwtLTEsGHD4O/vj1mzZsHExASdOnVStzHKBFQIIXIeGadknmbrvuY7c+PGjYiLi4ORkRFatWqFsWPHYvDgwerq3Ldv32LChAkwNjZGkSJFsrPoOZpmnKE5VDVv3ry4d+8eqlWrhgkTJqBWrVpISUnBtGnT0K1bN4SGhqJixYrv/AzxfpqV5ABw6tQpNG7cGKtXr0bz5s2RO3du7N+/HzY2Nur4r1ChQihTpow8CBI5hpGREVq0aIGSJUuiSpUq2LFjBwYMGIBNmzahVq1aWLVqFZo3b45Zs2ahR48eKFGiBFJTU7FmzRooigKSMsf8iEjC0dERycnJ6verZh6vWS8r9S3E35MRjsjRZBKaMbt27UK3bt0wfPhwNG3aFADg4uKCPHny4OHDh3jx4gXi4+Ph7u6OoKAghIaGokqVKnjw4AHCwsJkIJMJqampOj9rJpLu7u64efMmZs2ahZcvX6rXk5OT4eXlhf79+6NDhw4A/h84EEIIkbPJOOXvpaamqt97v//+OwBgw4YN8PT0xLhx47Bs2TIkJibCyMgIiYmJiImJQd26dXHv3j3ExMRAUZR0370i4yIjIxEVFYWff/4Zu3fvxpkzZ/Do0SN88803uHTpEr744gt8/fXXIIm5c+dmd3FznB07dqBz585ISkpC37590apVK8THx6Np06bYsGEDXr9+jRUrVuDBgwfqv4OwsDDY2dlJTmeRY+jr68Pb2xuDBg2CgYEBgoODcf/+fZQtWxZ9+/aFr68v3N3d0aZNG0RERGD48OE4d+4cDAwMkJKSInOfj0xRFFSrVg2Ghob47bffAPw/QK4oitS3EBkko3gh/gMsLCzg6emJY8eO4ejRo6hcuTKaNm2Ku3fvokaNGmjatCmePXuGRo0aoXz58ggKCgIAeHp6Ql9fX1bIZZD2tuT9+/fjwYMHsLS0hL29PZydnbFo0SK0aNECL1++RGBgIGxsbDB+/HiUKlUKX3/9taziF0II8Z+i/b353XffYcOGDfj+++9Rt25dREVFoW3btpg4cSIAoGXLlnj8+DHOnDkDe3t7hIaGSsqbTPr111/h7u6OggULqquZ4+LiULNmTVSqVEkdg+zevRtubm4ICQnBihUr4OfnBzMzM1SuXDm7/xNynEuXLuHu3buoUqUK7t69i8OHD6NgwYJISUlBQEAA1qxZg2bNmoEkBg0ahE6dOuHy5cs4e/Ys9PT0JOWNyDH8/PzQrl07hIeHIzQ0FHny5MGaNWvQqFEj2Nra4uDBg9iwYQOWLVuGNm3aAJC0IP+0Fy9e4MaNG9ldDCFyLMlhLsR/xOXLl9G3b1/o6+sjISEBL1++RFRUFJydnXH27FlcunQJkyZNwvXr1+Hv74+oqCgAMpDJKO3T4IcMGYK1a9ciKSkJ1tbWsLKywrRp01CiRAls3rwZwcHBSEhIgIGBAYoUKYIDBw7A0NBQ5zOEEEKI/4rhw4cjMjISERERcHFx0VlZ265dOxw7dgxDhgxB69at8ebNG+TLl08eMmdSREQEBgwYgClTpqB169YwMTEBSbRp0waPHj3C1q1bAQBJSUnIkycP1q5di759++LAgQM6h3tKADfzWrVqhVWrVsHf3x9LliyBmZmZuqpWT08P69atQ4sWLaCvrw8HBwccO3ZMDgsWOdKCBQuwaNEi/PTTT6hTpw7y5cuHTZs2wdjYGPfv38eePXsQFBQk7fpfsmzZMrRs2VLqW4gskoC5EP8hly9fRq9evXDkyBFERESgRYsWAP4/+Xn16hVu3LgBe3t7mYBm0aRJkzBjxgysWrUKnp6eCA4ORmhoKDw9PbFgwQJYW1vj7t27SExMRHx8PKpUqQI9PT2ZFAkhhPhPunDhApo2bYpJkyahfv366vXk5GQYGhoCAL766iv89NNPWL58OQICAgBAHjJnQa9evbB161YMGDAALVu2hJmZGX755RcEBQVh1qxZ6NKli3rvqlWrMG7cOOzatQsFCxbMxlLnPJq2mZycjJSUFISHh+Pp06fYvXs3ihUrhvHjx6NkyZJ48+YNcuXKBQCIjY3FpEmTsHv3bgmWixzN3d0dR48ehbe3N2JiYt554KS073+X1LcQWSMBcyH+Y65evYrevXtDT08Pw4YNg6enJ4D0X6SyaitjtFda3b9/H61bt0bv3r3RrFkz/PLLL2jevDlat26Nw4cPo1ixYli0aBGKFy+u8xlS10IIIf6r9uzZg0aNGuHIkSOws7PTCYS/evUKefPmBQCMGTMGI0aMkO/LLHj9+jVy584NAOjevTv279+PXr16oU2bNsiVKxdGjRqFtWvXYuDAgWjfvj0SEhLQvXt3kMSGDRvkwUQmaI8LtdsvAISGhmLlypWwtLTEhAkTYG1tDQDYt2+fOh4HJLglciZN37106VJMnDgRixcvhpubmzzcFELkWLKfToj/GFtbW8yePRskMW7cOOzfvx9A+oPJZEL690iqk6Lt27fDzMwMQ4cORdWqVXH48GF06dIFU6ZMQUREBLy8vLBlyxYEBATg4cOHOp8jdS2EEOK/QHudjubPpqamMDU1xfnz5wFATbUCAKtXr8batWsBACEhIdDX11dfExlDUg2WR0VFoXTp0rhy5Qq+++47rFixAvr6+ujfvz/atm2Lb7/9Fo6OjvDx8cH9+/cRGxsrh6pmkmZcOGHCBAQEBKBRo0aYN28eAKB3795o1aoV7t69i379+uHAgQOoV68ehgwZovNvQ4LlIifSBMV9fHzw+PFjNc2TBMuFEDmVBMyF+A+yt7fHrFmzoK+vj379+uH06dPZXaQcR3u1xMiRI/HNN9/g+vXrqFu3LqysrLBp0ybUqlULHTt2BJD2oMLPzw8BAQEoVKhQdhZdCCGE+NelpqbqBE6SkpIAADY2NihcuDBmz56Ns2fPAoB64PiKFSuwZcsWnc+Rh8yZo6nz0aNHo1+/frCyskJERASqVauGUaNGYeHChShUqBC+++47nDlzBjNmzMCcOXNw6NAhNTWI5Cz/e9oPFSZPnowpU6agatWqyJs3L4YMGYLhw4cDSEuL065dOzx9+hQtWrTAq1evsGPHDgkqis+GhYUFhg4diilTpqgPQoUQIieSx9dC/EfZ29tj8uTJiIyMhIuLS3YXJ8fRTGyuX7+Os2fPYtasWXBwcFBf/+OPP3DmzBkkJycjV65c2Lt3L3x9fdG/f38AkoZFCCHEf4d2moqpU6fi2LFjOH78ODp37oxWrVph7dq1qFGjBvr27QsPDw+ULFkSy5cvx5MnT/Dzzz9nc+lzNpJ48uQJ1qxZg3HjxuHLL78EkJYXvmPHjhgyZAgAoHnz5rC1tdU5cDUlJUVWO2eQpn0fO3YM+fPnx/Lly1G3bl0kJCRg2bJl6Nu3LwBg3Lhx6Ny5M+rWrYv4+Hi4uLjIWTbisxMQEICjR4/C0dExu4sihBBZJt/KQvyHOTk5YerUqQB0J7Pi/bRXls+ePRvTpk2Dubk5SpUqBeD/9ejj44OjR4+icuXKyJ8/P168eIGVK1eqnyHBciGEEP8VmvHFsGHDsHDhQgwePBi1atXC4MGDceDAAaxbtw67du3CDz/8gI0bN8LIyAg2NjbYunUrDAwM5CHzB1AUBYaGhjpjj6SkJOTJkweLFi3C+fPnMXPmTDx//hw9e/aEkZGR+l6p88zZs2cPatWqBTMzM8TGxgIATExM0L59ewBAv379oKenh7Fjx8LKygpWVlYA5MGE+PzY2tpi8eLFaoot6UuEEDmRfDMLIQBAguUZsHfvXhw5cgQA0KNHDzRr1gwzZszA4cOHceHCBZQqVUqtx0aNGoEkTp48idTUVHz33Xcy6RdCCPGfdezYMaxbtw7r1q2Dh4cHjh49iufPn6NJkyYA0gIsCxYsQHJyMpKSklCgQAEAcgBiZr1rAYSxsTEsLCzw448/onv37siTJw+Sk5NhaGiIUqVKYc+ePTh58iTy58+fTaX+PNjY2CAkJASTJk3Cb7/9ph7kmT9/frRv3x56enro1asXSpYsiS5duqjvk3Gh+BxpFhhJ+xZC5FQy+hRCiAxYsmQJvv/+e/j5+cHZ2Rn58uVDvnz5cOzYMVSuXBmjRo2CtbU1ypYtCyDtwKZmzZqhWbNm6mdIsFwIIcR/1du3b5EvXz54eHhg1apV6Ny5M2bPno2vvvoKiYmJ+O233+Dh4YH8+fPD0NAQQNqOLAmWZ5x2sPzkyZMwMjJC/vz5Ubx4cUyfPh116tRBUFAQYmJi1PGIoihYvXo1PDw8oCiKzk468X7vejBhbW2Nvn37IikpCSNGjED+/PnRs2dPAGlB87Zt28Lc3ByBgYHZUWQhhBBCZIKMQIUQ4m8sWbIE3bt3R3h4OJo0aaJuV540aRK8vLxw7NgxuLq6onv37oiIiICzszOA9JMpCZYLIYT4L7h//z4ePXqEU6dOwdXVFRYWFsibNy9u3bqF+fPn49tvv8XEiRPVYOKhQ4cwb948WFtb65wHIoHbzNGMOQYPHoyVK1ciISEBX3zxBTp06IAGDRogMjIS3bp1g6OjI+zt7XH37l08f/4c1apVg56enqTnyyDtepo7dy4uXryIixcvokuXLvD29kZISAgMDAwwePBgKIqCHj16AACMjIzUHRWyc0IIIYT4tMmISAgh/kJcXBwmT56MGTNmoF27dmqwvEWLFhgyZAhGjhyJS5cu4eTJk7h79y569OiBU6dOAZA0N0IIIf57YmJi1EMNe/fuDXd3d3To0AEvX75Ey5Yt0b17d/Tv3x+9evUCALx+/RozZsyAoiiws7PL5tLnTCTVP+/YsQNr1qxBVFQUZsyYAX19fYwZMwbr1q1D/fr1ceLECTRo0AA2NjaoVasWzp8/D319faSkpMi4JYO0H0yMHj0aRYoUgZWVFQYPHoyhQ4fCwMAAvXr1Qr9+/TB06FBMmTIl3WdIsFwIIYT4tCnUHmEJIYTQsWXLFnTv3h2bN2+Gg4MD9PT00Lt3b2zZsgUzZ87E9OnToaenh++++w6Ojo4oUqQIunbtitDQ0OwuuhBCCPGvmj9/PgYPHozhw4fD1dUVbm5umD17NpYvXw6SaNmyJS5duoSDBw/iu+++w5MnT7B582bcuXMHJ06cgKGhoaxy/gDr1q3Dli1bULJkSQwZMgQAcPz4ccycORPnzp3Dt99+i5YtW6Z7n6x2zrxdu3aha9euiI6OhpubG3bs2IF69eph8eLFaNOmDQDgyZMnGDNmDM6fP48tW7bIjgkhhBAiB5GAuRBC/IVx48Zh+vTp+OOPP9Rr9+7dQ0pKCiwtLREXF4euXbvizZs3OHToEOLj42FiYiLpV4QQQvynzJ8/H3369MGKFSsQFBSk89rKlSsxZcoU5MuXD7169cKePXvw888/w87ODqVLl0ZYWBgMDAwkcPsBrl27ho4dO+L06dPo0qULJk+erL52/PhxzJo1C3FxcejevTs6deqUjSXNeb777js0a9ZMTbkHAD///DPGjx+PAwcOYOXKlejatauaZigxMREnTpyAl5cXnjx5goIFC0p+eCGEECKHkeUbQgjxF+zs7PDq1Sts3bpVvVa8eHFYWloiNTUVTk5OaNiwIYoUKYJnz57BzMxM3doshBBC/Bfs2rUL3bt3x/DhwxEUFASSIIm3b98CAFq2bIl27drh1KlTMDQ0RGhoKI4dO4YdO3YgMjJSguUfQenSpTFq1ChUqVIF69atw7Zt29TXKlWqhG+++Qbm5ubYv39/NpYy59m+fTsuXLigk1sfAB49egSS2Lp1K7p164bx48erOfl37NiB6Oho3Lt3D2ZmZhIsF0IIIXIgWWEuhBB/4dq1a6hYsSJ8fX0xbdo0WFtb67z+/PlzfPnll7C3t8e0adOyqZRCCCFE9rl8+TI6d+4MMzMzDBw4EF5eXupr2ilWypUrBy8vL8ydOxfJyckwNDQEAAkmZpJ2nf657rZt24bp06cjOTkZQ4YMQe3atdXXLl26BDs7O0l5k0matrp+/XoULVoUHh4eePHiBVxcXHDjxg1ERUWhXbt2ANJy8gcFBaFgwYJYsmSJtGshhBAih5LRkhBC/AXNVvENGzZg2LBhOHnypPrajRs30LRpU9y6dQuTJk0CoHvwlhBCCPFfYG9vjwULFuD169cYN24c9u3bp76mCRg+e/YMSUlJKF68OACowXLte8Tf0w6WL1y4ED179kTfvn2xfPlyAECdOnXQp08f5MqVCxMmTMDOnTvV92rOYklNTc2WsuckQ4YMUfPAGxoa4uzZsxg4cCBmz56NI0eOIH/+/Jg2bRosLCywYsUK7NixA6tXr0ajRo1w8+ZNLF68WF1ZLoQQQoicRwLmQgjxN1q0aIE5c+Zg1apVCAwMhL+/P3x9fdGyZUs8e/YMR44cgYGBAVJSUmTSL4QQ4j/J3t4es2bNgqIo+P7779Ol/rh27RosLS1RrVo1APKAOas0wfLBgwdj6NCh0NfXx927dzFp0iSMGDECAODv748+ffogb968GDhwII4fP/7OzxDvlpCQgHv37mH37t2YOHEiAMDFxQVjx47FtWvXMHPmTJw+fRpNmjTB0qVL8eDBA3Ts2BGTJ0+GiYkJjh8/LuNCIYQQIoeTlCxCCJFBJ0+eRGRkJC5dugRra2tUqlQJ3bt3h76+vuReFUIIIZCWnqVv374gieHDh8PLywtv375Fo0aNoKenh/Xr10vA9gMtXLgQ48ePx/Lly1GlShUsX74cHTt2RLFixdCkSRPMmDEDABAbG4v9+/dj4sSJUueZ9PDhQ4wbNw4nT55EnTp1MHLkSABAdHQ0pk6dijJlyiA4OBiurq4A0h4IFSxYEKamplAURcaFQgghRA4nAXMhhPhAKSkp0NfXz+5iCCGEEJ8ETdBcT08Pw4YNw7Rp03DhwgWcPHkShoaGOmlFROaNGzcOSUlJGDt2LNavX4+OHTti6NChePz4McLDw9G7d298//33Ou+ROs8Y7THd9u3bERERgePHj6Nnz54YMGAAgP8HzZ2cnNCnTx+4u7vrfIbUtRBCCJHzScBcCCEyQQ4mE0IIIf7e5cuX0b9/f2zZsgWlS5fGmTNnYGhoKCtvM2nLli3YunUrnj17Bh8fH7Rq1Qqpqam4ceMGcufOjXr16qF9+/YIDg5WV0O/evUKY8aMQXBwcHYXP8caOHAgTp06BT09PZw8eRL58uVDt27dMGzYMADAypUrMX36dBQuXFhdcS6EEEKIz4eMVoUQIhMkWC6EEEL8PXt7e0yZMgVz587FtGnTYGBgIMHyTJo/fz6GDRsGT09P3LhxAwsXLsSLFy/QuXNnlCpVCtu3b8ebN2/QqlUrAMDbt29Ru3ZtBAUFoXnz5tlc+pxr1apVWLhwIbZs2YJy5crh2bNnGDRoENatWwd9fX0MHjwYLVu2xMuXL7F//37Y29tnd5GFEEII8ZHJXjEhhBBCCCHER+fo6IhZs2ZJsDwLIiMj0adPH4SFhWHdunX48ccfUaxYMSxbtgwvXrwAABgZGeHly5eIjo7GnTt3MGrUKBQoUAAtW7aEvr4+UlJSsvm/Ime6fv06SpYsiYoVKyJPnjwoWrQovv/+exQrVgwzZ87E9OnTAQAdO3ZEZGQk9PT0kJqams2lFkIIIcTHJAFzIYQQQgghxD9KguUZt2vXLnTr1g3Dhw9H06ZNAQAuLi7IkycPHj58iBcvXiA+Ph7u7u4ICgpCaGgoqlSpggcPHiAsLAyKooCknK+SSZqgd9GiRZGSkoLbt2+r1y0tLTFs2DC8fPkSs2bNwsKFCwGkpeoDIDnLhRBCiM+MjFyFEEIIIYQQ4hNhYWEBT09PHDt2DEePHkXlypXRtGlT3L17FzVq1EDTpk3x7NkzNGrUCOXLl0dQUBAAwNPTE/r6+rKaP4P+fDin5s/u7u64efMmZs2ahe+//x758uUDACQnJ8PLywu+vr7o0KEDAEnVJ4QQQnyu5NBPIYQQQgghhPiEXL58GX379oW+vj4SEhLw8uVLREVFwdnZGWfPnsWlS5cwadIkXL9+Hf7+/oiKigIApKSkyMryDNAOlu/fvx8PHjyApaUl7O3tUbBgQcTExKBFixbo0qULAgMDYWNjg+DgYJQqVQqhoaFQFEXqWgghhPiMScBcCCGEEEIIIT4xly9fRq9evXDkyBFERESgRYsWAP4f7H316hVu3LgBe3t7CdxmAkl1ZfiQIUOwdu1aJCUlwdraGlZWVpg2bRpKlCiBzZs3Izg4GAkJCTAwMECRIkVw4MABGBoa6nyGEEIIIT4/EjAXQgghhBBCiE/Q1atX0bt3b+jp6WHYsGHw9PQEgHRpV2S1c+ZNmjQJM2bMwKpVq+Dp6Yng4GCEhobC09MTCxYsgLW1Ne7evYvExETEx8ejSpUq0NPTk5Q3QgghxH+ABMyFEEIIIYQQ4hOlSc8CACNGjECNGjWyuUQ5k3Yalvv376N169bo3bs3mjVrhl9++QXNmzdH69atcfjwYRQrVgyLFi1C8eLFdT5DHkwIIYQQ/w1ynLcQQgghhBBCfKLs7e0xa9Ys6Ovro1+/fjh9+nR2FynHIakGy7dv3w4zMzMMHToUVatWxeHDh9GlSxdMmTIFERER8PLywpYtWxAQEICHDx/qfI4Ey4UQQoj/BgmYCyGEEEIIIcQnzN7eHpMnT4a3tzdcXFyyuzg5ina+8ZEjR+Kbb77B9evXUbduXVhZWWHTpk2oVasWOnbsCACwtbWFn58fAgICUKhQoewsuhBCCCGyiSRfE0IIIYQQQohPnJOTE6ZOnQpAN72I+GuaYPn169dx9uxZzJo1Cw4ODurrf/zxB86cOYPk5GTkypULe/fuha+vL/r37w9A0rAIIYQQ/0USMBdCCCGEEEKIHESC5X9Pe2X57NmzMW3aNJibm6NUqVIA/v/QwcfHB0ePHkXlypWRP39+vHjxAitXrlQ/Q4LlQgghxH+PHPophBBCCCGEEOKzsXfvXhw5cgQA0KNHDyQkJMDT0xO///47Nm7cCH9/f/Xet2/fIjY2FidPnkRqaiq+++47GBgYyMpyIYQQ4j9MAuZCCCGEEEIIIT4LS5Yswffffw8/Pz84Ozuje/fuAICnT5+icuXKKFiwIBYvXoyyZcu+9zMkWC6EEEL8t0nAXAghhBBCCCFEjrdkyRJ0794d4eHhaNKkCYyMjAAAkyZNgpeXF5ydneHq6goLCwtERETA2dkZgOSEF0IIIYQuCZgLIYQQQgghhMjR4uLi0LJlS/Tp0wfdunVTr7do0QJr1qxB7dq1MX78eDg4OKBixYqwtLTE7NmzUaFChWwstRBCCCE+RfIYXQghhBBCCCFEjnbr1i08f/4c3t7eSE1NBQD07t0bJ06cwIYNG6AoCkaMGIELFy7gxIkT+O233xAREZHNpRZCCCHEp0hWmAshhBBCCCGEyNHGjRuH6dOn448//lCv3bt3DykpKbC0tERcXBy6du2KN2/e4NChQ4iPj4eJiYnkKhdCCCFEOrLCXAghhBBCCCFEjmZnZ4dXr15h69at6rXixYvD0tISqampcHJyQsOGDVGkSBE8e/YMZmZm0NfXR0pKSjaWWgghhBCfIgmYCyGEEEIIIYTI0apUqQIDAwOEh4fj5s2bOq/p6enh+fPn2Lt3L8qUKQMTExP1NVlhLoQQQog/M8juAgghhBBCCCGEEB+idOnSCAsLQ8eOHZEnTx4EBwfD1dUVAHDjxg107doVDx8+xLp16wAAJKEoSjaWWAghhBCfKslhLoQQQgghhBAix0tJScGiRYvQq1cvmJubw8XFBW/fvsXz588BAHv37oWhoSFSUlJkZbkQQggh3ksC5kIIIYQQQgghPhsnT55EZGQkLl26BGtra1SqVAndu3eHvr4+3r59CwMD2WgthBBCiPeTgLkQQgghhBBCiM+erCwXQgghREZIwFwIIYQQQgghxGdFcpQLIYQQIqv0srsAQgghhBBCCCHExyTBciGEEEJklQTMhRBCCCGEEEIIIYQQQghIwFwIIYQQQgghhBBCCCGEACABcyGEEEIIIYQQQgghhBACgATMhRBCCCGEEEIIIYQQQggAEjAXQgghhBBCCCGEEEIIIQBIwFwIIYQQQgghhBBCCCGEACABcyGEEEIIIYQQQgghhBACgATMhRBCCCGEEEIIIYQQQggAEjAXQgghhBBCCCGEEEIIIQBIwFwIIYQQQgghhBBCCCGEACABcyGEEEIIIYQQQgghhBACgATMhRBCCCGEEEIIIYQQQggAEjAXQgghhBBCCCGEEEIIIQBIwFwIIYQQQgghhBBCCCGEACABcyGEEEIIIYQQQgghhBACgATMhRBCCCGEEEIIIYQQQggAEjAXQgghhBBCCCGEEEIIIQBIwFwIIYQQQgghhBBCCCGEACABcyGEEEIIIYQQQgghhBACgATMhRBCCCGEEEIIIYQQQggAEjAXQgghhBA5gKIoqFWrVnYX45MxevRoKIqCXbt2ZXdRAABTpkxB7ty5cevWrewuSo7VoUMHKIqC69ev/yOff/36dSiKgg4dOuhcr1WrFqpWrQqS/8jfK4QQQgiR00jAXAghhBBC4OXLl/jhhx9QqVIlGBkZIU+ePLC0tISXlxeGDh2Kq1ev/qN/f61ataAoyj/6d3wsmsCj9v8MDQ1hYWGBFi1a4OjRox/8d+zatQuKomD06NEfXuB/2JMnTzBu3Dh06dIFVlZW76yfv/qfjY3NRy9TVh8oaILW2v8zNjZGlSpVMH36dCQnJ3/0sma3kJAQHD58GNHR0dldFCGEEEKIT4JBdhdACCGEEEJkr+fPn8PT0xOnT5+GnZ0d2rZtC1NTU9y6dQvnzp3DhAkTYGtrC1tb22wrY1xcHPLly5dtf/+72Nraom3btgCAFy9e4NixY1i9ejViY2Oxbds2eHt7/2N/d58+fdCqVStYW1v/Y39HRk2dOhUJCQkYOHAgAMDU1BQhISE69zx9+hQzZ85EyZIl061wNjU1/ZdKmnGdO3eGpaUlUlNTcfv2bcTExGDAgAHYuXMnfvrpp3/k7xw/fjyGDBkCCwuLf+Tz38fHxwdubm4YNWoUWrVqlWMeXAkhhBBC/FMkYC6EEEII8R83Y8YMnD59Gp07d8b8+fPTBcx+//13vH79OptKl8bR0TFb//53sbOzS7cCfMKECRg6dChGjhyJ3bt3/2N/d+HChVG4cOF/7PMzKjk5GQsXLkSNGjVQunRpAGkB8D/Xy/Xr1zFz5kzY2NjkiFXzXbp0QbVq1dSfv//+e1SsWBE///wzdu/ejZo1a370v7N48eIoXrz4R//cjGjbti369++P7du3o06dOtlSBiGEEEKIT4WkZBFCCCGE+I87ePAggLRVy+9aXVqqVKl3BqzPnTuHli1bomjRosidOzdKlSqF/v3748mTJ+nutbGxgY2NDZ4+fYq+ffvCysoKBgYGWLx4MRRFUYPL2qkwtFcivyuHuXbO57lz58LJyQl58uRByZIlMWbMGKSmpqYrx9u3bzF9+nRUqFABefPmhYmJCXx8fLBx48bMVNl7de7cGQBw7NixdK8tXLgQjRo1go2NDfLkyQMzMzPUq1cPO3fu1Llv9OjR8PHxAQCMGTNGp040+a3/KuXIhg0b4OPjAxMTE+TNmxeurq6YMWMGUlJS0t27c+dO+Pv7o0SJEsidOzdKlCiBWrVqITIyMkP/vb/88gvu37+P5s2bZ+j+d3nz5g2mTZuGSpUqIX/+/ChQoAC8vLzeuZI7ISEBo0aNgrOzM4yMjGBiYgJHR0d07NhRzZ9eq1YtjBkzBkDa6umPkfqlRIkSCAoKAgAcOXJEvf7w4UP0798fdnZ2yJ07NwoXLoymTZvi7Nmz6T7jr/4NAH+dwzwqKgrVqlWDkZERjIyMUK1aNURFRb2zrCkpKZg4cSLs7OyQJ08e2NnZYfz48e/896DRokULAMCiRYsyWiVCCCGEEJ8tWWEuhBBCCPEfZ2ZmBgC4cuUKXF1dM/SeAwcOoG7dunj9+jWaNWsGGxsb/Pbbb5gxYwY2btyIgwcPolChQjrvef36NWrXro3nz58jMDAQuXLlgrm5OUJCQrB48WLcuHFDJ5VHRsvy7bffYteuXWjQoAHq1q2L2NhYjB49Gm/evMG4cePU+0iiZcuWiImJ+R97dx4XVbn/AfxzZgaQnUBQQUVBDc0ld9Nropli6nXP9N7U0rqZllpq5YppmZbeLKt7LUsss3JtMbFcM7c0d3MLd9QLooAiAjNzfn/4mxPjDJvCdxY+7/vydfOZMzPP8zkPX888czgHderUwYgRI5CVlYVvvvkG3bp1w7x58/Diiy8W6z2LYjDYHmaPGDECjRo1QseOHREaGork5GSsXr0aHTt2xMqVK9GjRw8Atxd8z5w5g4SEBLRr187qi4KiLl8yb948jB49GsHBwRg4cCB8fX3x/fffY8yYMdi6dSuWL1+ufSmyZs0adO/eHUFBQejRoweqVKmC1NRU7N+/H0uWLMGwYcOKHOeGDRsAwOps7JLIyclBXFwcNm/ejMaNG2Po0KHIy8vDmjVr0KNHD7z//vsYOXIkgNv7r3Pnzti1axfatGmDuLg46HQ6nDlzBqtWrcLgwYNRrVo17YuWLVu2YPDgwdpCeWlf+iUpKQmxsbFITk5Gp06d0LNnT6SkpGDFihVYt24dNmzYgJYtW9qM197PQGHGjBmDd999FxERERg6dCgURcGKFSswZMgQHDhwAHPnzrXa/tlnn8Wnn36KmjVrYsSIEbh16xbmzp2L7du3F/ge4eHhqF69us2XN0RERETlkkpERERE5drq1atVAGpAQID6yiuvqBs2bFCvXr1a4PYmk0mtXbu2CkBNTEy0euy1115TAahDhw61ao+MjFQBqJ06dVJv3rxp85rt2rVTCzs0BaC2a9fOqm3w4MEqALVmzZrqxYsXtfbU1FQ1KChI9ff3V3NycrT2xYsXa6+Tv/38+fNqWFiY6uHhoZ46darAPlicPn1aBaB27tzZ5rHp06erANSuXbvaPGbvtS9evKiGh4ertWvXtmrftGmTCkCdOnWq3T5MnTpVBaBu2rRJa0tKSlINBoMaFhamnjt3TmvPycnR8v3888+19t69e6sA1AMHDti8/pUrV+y+752aN2+u6nQ6qzztsWR25z6cMGGCCkCNj49XzWaz1p6Zmak2a9ZM9fT0VJOTk1VVVdWDBw+qANRevXrZvP6tW7fU69eva3+3l09xWObUjh07rNqTk5PVsLAwFYC6efNmVVVVtXXr1qrBYFB/+uknq22PHz+u+vv7qw0aNLBqL+pnwPLep0+f1tp++eUXFYBat25dNT09XWtPT09XY2JiVADq1q1btXbLvGnUqJF648YNrf3ChQtqxYoVVQDq4MGD7Y69V69eKoBi/QwQERERuTNekoWIiIionOvRowdmz54Ns9mMWbNm4ZFHHkFwcDBq1aqFkSNH4uTJk1bbb9u2DSdPnkSXLl3QuXNnq8cmTpyIkJAQfPnll8jNzbV5r7fffhve3t6l2v/JkydbXfu5YsWK6NGjB65fv47jx49r7ZZLX8yePRuenp5ae9WqVTFmzBjk5eVhyZIlxX7fP//8E/Hx8YiPj8e4ceMQGxuLyZMnIywsDG+//bbN9jVr1rRpq1KlCvr06YOTJ0/i7NmzxX5ve5YsWQKj0YiXX34Z1apV09o9PT3x1ltvAfgrg/zs7Y87fzugIBcuXEBQUJBVnsVlNpvx0UcfoVatWpgyZYrV5YD8/f0xZcoU5ObmYuXKlUX218vLC35+fiXuQ0E++eQTxMfHY+rUqXj66adRr149pKSk4O9//zvatWuHffv2Yfv27Rg8eDAeffRRq+fWqVMHzzzzDA4dOmT30iwl+Rmw7K/4+HgEBgZq7YGBgdpvY+Tfp4sXLwYATJkyBb6+vlp7REQERo0aVeh7Wc50v3DhQrH6RkREROSueEkWIiIiIsK4cePw3HPPITExEdu3b8eePXuwa9cufPDBB1i4cCG+/vpr/P3vfwcA7Nu3DwBsrikOAL6+vmjWrBnWrVuHEydOoH79+tpjFSpUQIMGDUq9702aNLFpq1q1KgAgPT1da9u3bx+8vb3RokULm+0tY9m/f3+x3zcpKUm7VrZFWFgYtm7dijp16thsf+rUKcycORMbN25EcnKyzY1UL168iMjIyGK//50K2y+tWrWCt7e31fgef/xxrFy5Ei1btsSAAQPQoUMHtG3bFmFhYcV+z7S0NKvF+ZI4fvw4rl27hvDwcJscASA1NRUAcOzYMQBA3bp10aBBA3z55Zc4f/48evbsibZt26JJkybQ6/V31YeCLFy4UPtvf39/xMTEYODAgdrlYXbu3AkAuHz5st2bmFr6fOzYsXv6GShsn9qbswcOHAAAtG3b1mZ7e235WS7NdOXKlWL3j4iIiMgdccGciIiIiADcXhjs16+fdgPHjIwMTJgwAR9++CGGDh2K5ORkeHp6IjMzEwAKvPZy5cqVtefnFxYWZvemovcq/5m3FpZriOe/0WVmZmaBi7sF9bkwnTt3RmJiIoDbi7sJCQl45ZVX0LNnT/z2229WZzz/+eefaNGiBTIzM9G+fXt0794dAQEB0Ol02Lx5M7Zs2WKzgF5SRe2XsLAwJCcna3/v378/PDw88O677+K///0vPvzwQ+3mqnPnzi3WNeS9vb2RnZ19V/213Bz2yJEjOHLkSIHbZWVlAbi9Tzdu3Ij4+HisXLkSL7/8MoDbv1HwwgsvYOLEiaW2cL5jx45Cr8tu6fuaNWsKvWGspe8WJf0ZyMzMhE6nQ2hoqM1jlSpVgk6ns5qzGRkZ0Ol0qFixot3tC2PZjz4+PsXuHxEREZE74iVZiIiIiMiuwMBAzJ8/H5GRkbhy5QoOHToEAAgICAAA/O9//7P7PEu7ZTuLslgsL4mAgIAS97m4QkNDMXbsWEyYMAFHjx7FpEmTrB7/97//jWvXriEhIQE///wz3n33Xbz++uuIj49HTEzMXb3nnYraLykpKTbj6927N3755RdcvXoVa9euxbBhw7BlyxZ07tzZ6uz8goSGhmqLx3fb3z59+kBV1QL/fPbZZ9pzKlasiPnz5yM5ORl//PEH5s+fj5CQEEydOhWzZ8++q37cS9/ff//9Qvs+ePBgq+eV9GcgICAAZrNZO9s+v5SUFJjNZqt9GhgYCLPZbPcs8YLmhYVlP9pbnCciIiIqT7hgTkREREQFUhTF5ozTxo0bAwA2b95ss/3NmzexZ88eeHt74/777y/2+1jODM5/Rnhpa9y4MbKzs/Hbb7/ZPLZlyxYAKNZZ1YWZMGECwsPD8eGHH+LMmTNae1JSEgBol7WxMJvN2LZtm83r3E0ehe2X3377DdnZ2QWOLyAgAHFxcViwYAGGDBmClJQU7Nq1q8j3bNCgAW7dunVX172uW7cuAgICsGfPHuTl5ZXouYqioG7duhgxYgR+/vlnAMB3332nPV7W86lly5YAbp+JXpYK26f25myjRo0AAFu3brXZ3l5bfsePH4eHh0epfYFDRERE5Kq4YE5ERERUzv33v//F7t277T62cuVKHDt2DEFBQdq1mNu0aYPo6GisXbsW69evt9p+5syZuHLlCgYMGFCiG0Farp9cljcctJzt+9prr1kt0CYnJ2Pu3LkwGAz4xz/+cU/v4e3tjVdeeQV5eXmYPn261m65Nvmvv/5qtf2sWbPs3hjybvIYOHAgDAYD5s6di4sXL2rteXl5ePXVVwEAQ4YM0do3bNiAW7du2bxOSkqKNpaitGvXDgDsfglRFIPBgOHDh+Ps2bMYO3as3UXzw4cPa/05ffo0/vjjD5ttLGdO5+9vWc+nFi1aoGXLlli6dCm+/vprm8fNZrO2oH0vLHN22rRp2iV3gNuXarFc9z3/WeyDBg0CALz++utWl4NJTk7GvHnzCnyfvLw87Nu3D82aNeMlWYiIiKjc4zXMiYiIiMq5tWvX4rnnnkOtWrXQpk0bhIeH48aNG9i/fz+2bt0KnU6HDz/8EF5eXgAAnU6HRYsWoXPnznjsscfQr18/REZGYteuXdi4cSOio6Px1ltvlagPHTp0wPLly9GvXz889thj2s0Ru3btWmrjfPLJJ7Fy5Up8++23aNiwIbp164asrCx88803SEtLw5w5cxAVFXXP7/Pss89i1qxZWLx4MSZMmIDo6Gg899xz+Oyzz9C7d2/0798fISEh2LlzJ/bu3YuuXbvaXAc7JiYG4eHh+Oqrr+Dj44OqVatCURQMHz7c7jXbASA6OhqzZs3Cyy+/jIYNG+Lxxx+Hr68vfvjhBxw7dgw9evTAP//5T237l19+GefOnUNsbCxq1KgBRVHw66+/4rfffkPr1q3Rpk2bIsfao0cPjBkzBuvXr0fv3r1LnNW0adOwd+9evPfee1izZg3atWuH0NBQJCcn49ChQzhw4AB27NiBsLAwHDhwAL169ULz5s1Rv359VK5cGcnJyVi9ejX0er12TXMAaN++PRRFwcSJE3Hs2DEEBgYiMDAQw4cPL3EfC7J06VK0b98eTzzxBN599100bdoUFSpUwLlz57Bjxw6kpqba/UKiJB5++GG88MILeP/991G/fn3t8jUrV67E+fPn8eKLL+Lhhx/Wto+NjcVTTz2Fzz77DA0aNECvXr2Qk5ODr7/+Gq1atcIPP/xg931++eUX5OTkoGfPnvfUXyIiIiK3oBIRERFRuXbs2DF19uzZ6qOPPqrWrFlTrVChglqhQgU1OjpaHTx4sLpnzx67zzt48KDat29ftWLFiqqHh4caGRmpvvjii2pqaqrNtpGRkWpkZGSBfcjLy1PHjx+vVq9eXTUYDCoAdfDgwdrjANR27dpZPWfw4MEqAPX06dM2rzd16lQVgLpp0yab93nnnXfUBg0aqF5eXqq/v7/arl079dtvvy2wb3c6ffq0CkDt3Llzgdu8//77KgD1ySef1No2bdqktmnTRvX391eDgoLUxx57TP39998L7OvOnTvVdu3aqf7+/ioAq7EW9BxVVdVvv/1We56Xl5faoEEDdc6cOWpeXp7Vdl999ZX6+OOPq9HR0aqPj48aGBioPvjgg+rs2bPVGzduFDuPzp07qyEhIWpubm6B21gyu3MfqqqqGo1G9b///a/apk0bNSAgQPXy8lKrV6+uxsXFqR999JHWl/Pnz6uvvvqq2qpVKzUsLEz19PRUq1evrvbt21fdtWuXzesuWrRI288ACp1/FpY5tWPHjmKN/erVq+qkSZPU+vXrq97e3qqfn59au3ZtdeDAgerKlSutti3qZ6Cw+fzpp5+qzZs3V318fFQfHx+1efPm6qeffmr3dYxGozpz5kw1KipK9fT0VKOiotQ333xT/fPPP21+riyGDBmienp6qikpKcUaNxEREZE7U1RVVR2wTk9ERERERG7gp59+QufOnfHVV1+hf//+ju4OlVB6ejqqV6+Ovn374tNPP3V0d4iIiIgcjgvmRERERER0Tzp16oSLFy/i4MGD0Ol4myRXMnXqVLzzzjs4ceIEIiIiHN0dIiIiIofj0SwREREREd2T999/H3379sWlS5cc3RUqofvuuw+LFy/mYjkRERHR/+MZ5kRERERERERERERE4BnmREREREREREREREQAuGBORERERERERERERASAC+ZERERERERERERERAC4YE5EREREREREREREBIAL5kREREREREREREREALhgTkREREREREREREQEgAvmREREREREREREREQAuGBORERERERERERERASAC+ZERERERERERERERAC4YE5EREREREREREREBIAL5kREREREREREREREALhgTkREREREREREREQEgAvmREREREREREREREQAuGBORERERERERERERASAC+ZERERERERERERERAC4YE5EREREREREREREBIAL5kREREREREREREREALhgTkREREREREREREQEgAvmREREREREREREREQAuGBORERERERERERERASAC+ZUxjZu3Iinn34aMTEx8PX1RUREBHr06IHff//dartff/0Vw4YNQ9OmTeHl5QVFUXDmzJliv8/EiRPRuHFjBAcHo0KFCoiKisKzzz6Ls2fP2mybl5eHadOmoUaNGvDy8kJMTAzef//9ex0qERERERERERERuTiDoztA7u2jjz5CWloaRo0ahXr16iE1NRVz5sxBq1atsG7dOnTo0AEAsGHDBqxfvx6NGzdGQEAANm/eXKL3SU9Px4ABA1C3bl34+/vjjz/+wIwZM/Ddd9/hyJEjCAkJ0bZ9/vnn8fnnn2P69Olo3rw51q1bh1GjRuH69euYMGFCaQ6fiIiIiIiIiIiIXIiiqqrq6E44O7PZjIsXL8Lf3x+Koji6Oy4lNTUVoaGhVm03btxA48aNUbduXXz33XcAbmes093+hYf33nsPkydPxsGDBxEZGXnX7/3zzz+jb9++mD9/Pp588kkAwNGjR/HQQw9h8uTJePnll7VtX3zxRXzzzTf4448/EBwcfNfvSURERERERERERM5FVVVcv34d4eHh2hpkQbhgXgwXLlxAtWrVHN0NIiIiIiIiIiIiIrpL58+fR9WqVQvdhpdkKQZ/f38AtwMNCAhwcG9cX0ZGBho0aIC2bdtiyZIlNo8X5wxzo9GIffv2oXHjxjAYDFbteXl5OHHiBMaPH48rV65gy5Yt8PPzAwA8/fTT2LJlC5KSkqxeLysrC+Hh4XjppZcwderUUhyteygobyobzFsW85bFvOUxc1nMWxbzlsW85TFzWcxbFvOWxbxlMW/nk5mZiWrVqmnrvIXhHisGy2VYAgICuGBeCp5//nlkZWUhPj7ebp4VKlQAcPuLioLyNhqN8PX1RUBAgFZ4Ll++jCpVqmjbtGzZElu2bEF4eLjWlpmZiZCQEJvXDQgIgKenJ27cuMF9bIe9vKnsMG9ZzFsW85bHzGUxb1nMWxbzlsfMZTFvWcxbFvOWxbydV3Eut134BVuIStnkyZOxZMkS/Pvf/0bTpk3v+nV0Oh1CQ0OtrjlUsWJF7N69G7/++is+/vhjXL16Fe3bt8elS5esnlvYDwavUW+fvbyp7DBvWcxbFvOWx8xlMW9ZzFsW85bHzGUxb1nMWxbzlsW8XRv3GomZNm0aZsyYgTfeeAMjR468p9fS6XSIjo62KjwGgwHNmjVDmzZtMGzYMGzcuBGnTp3CW2+9pW0TEhKCtLQ0m9fLyspCbm4ub/hZAHt5U9lh3rKYtyzmLY+Zy2Lespi3LOYtj5nLYt6ymLcs5i2Lebs27jUSMW3aNMTHxyM+Ph4TJky459czm81ISkqC2WwucJuqVasiPDwcJ06c0NoaNGiA1NRUXL582WrbQ4cOAQDq169/z31zR8XJm0oP85bFvGUxb3nMXBbzlsW8ZTFvecxcFvOWxbxlMW9ZzNu1ccGcytz06dMRHx+PSZMmldoNNc1mM1JTUwstPH/++ScuXLiAWrVqaW09evSAoihISEiw2nbRokXw9vZGXFxcqfTP3RQnbyo9zFsW85bFvOUxc1nMWxbzlsW85TFzWcxbFvOWxbxlMW/XxqvOU5maM2cOpkyZgri4OHTt2hU7d+60erxVq1YAgNTUVGzZsgXAX2d7r127FqGhoQgNDUW7du205xgMBjz88MN48803AQAHDx7EmDFj0LdvX0RFRUGn0+HQoUP497//jZCQEIwdO1Z77gMPPIChQ4di6tSp0Ov1aN68OX766ScsWLAAM2bM4CVZiIiIiIiIiIiIyjEumFOZ+v777wEAiYmJSExMtHlcVVUAwJEjR9CvXz+rx55//nkAQLt27bB582at3WQywWQyaX+vVKkSwsPDMWfOHFy6dAlGoxFVq1ZFt27dMGHCBFSrVs3qdT/88ENERETg/fffx+XLl1GjRg3MmzcPL7zwQqmMmYiIiIiIiIiIiFyTolpWLKlAmZmZCAwMREZGBgICAhzdHcLtX225ePEiwsPDeQMFAcxbFvOWxbxlMW95zFwW85bFvGUxb3nMXBbzlsW8ZTFvWczb+ZRkfZcL5sXABXMiIiIiIiIiIiIi11SS9V1+xUEuyWQy4ejRo1aXZqGyw7xlMW9ZzFsW85bHzGUxb1nMWxbzlsfMZTFvWcxbFvOWxbxdG69hTsUS/U60o7tgxUPxwKDKg7D4x8XIU/Mc3R0AQNLYJEd3ocyoqoqMjAzwF1JkMG9ZzFsW85bHzGUxb1nMWxbzlsfMZTFvWcxbFvOWxbxdG88wJyIiIiIiIiIiIiICF8yJiIiIiIiIiIiIiABwwZxclEk1YWvGVphUXgtKgk6nQ1RUFO/sLIR5y2Lespi3PGYui3nLYt6ymLc8Zi6Lecti3rKYtyzm7doUlRfTKVJJ7qLqrpztGubOyJ2vYU5EREREREREROSqSrK+y685yCUZFAP6hPaBQeF9ayWYTCYcOHCAd3cWwrxlMW9ZzFseM5fFvGUxb1nMWx4zl8W8ZTFvWcxbFvN2bVwwJ5ekQEGQIQgKFEd3pVxQVRXZ2dm8u7MQ5i2Lecti3vKYuSzmLYt5y2Le8pi5LOYti3nLYt6ymLdr44I5ERERERERERERERG4YE5EREREREREREREBIAL5uSijKoRiVcTYVSNju5KuaDX6xETEwO9Xu/orpQLzFsW85bFvOUxc1nMWxbzlsW85TFzWcxbFvOWxbxlMW/Xpqi8mE6RSnIXVXcV/U60o7vg9JLGJjm6C0RERERERERERHSHkqzvuvwZ5jNnzkTz5s3h7++PsLAw9OzZE8ePH7faRlVVxMfHIzw8HN7e3oiNjcWRI0cc1GMqDR6KBwZVHgQPxcPRXSkXjEYjdu/eDaORZ/RLYN6ymLcs5i2Pmcti3rKYtyzmLY+Zy2Lespi3LOYti3m7NpdfMN+yZQtGjBiBnTt34ueff4bRaESnTp2QlZWlbTN79mzMnTsX8+fPx+7du1G5cmU8+uijuH79ugN7TveKi+WyTCaTo7tQrjBvWcxbFvOWx8xlMW9ZzFsW85bHzGUxb1nMWxbzlsW8XZfB0R24V4mJiVZ//+yzzxAWFobff/8dDz/8MFRVxbvvvouJEyeid+/eAICEhARUqlQJX375Jf71r385ottERERERERERERE5GRcfsH8ThkZGQCA4OBgAMDp06dx+fJldOrUSdvGy8sL7dq1w/bt2+0umOfk5CAnJ0f7e2ZmJoDbv05h+VUKnU4HnU4Hs9kMs9msbWtpN5lMyH95+ILa9Xo9FEWx+RUNy00B7vw2qqB2g8EAVVWt2hVFgV6vt+ljQe2FjQkADIoBChSt3aSaYIbZpt2oGqFCtTkDvKD2PDUPChQYFEOR7SpUGFUjdNBBgaK9Vv52vfLXDRXMqhkmmKCHHjrlr1+oKKjv9zKm/PvQUfuprOae5bmqqtps76pjKqzd0WOyPGYymdxmTM68nwDYvI6rj8mZ91P+93GXMRXWd2cY0501xR3GVJx2R40JsK0prj4mZ95PhdUUVx1T/j4645gs/+9OYyqq744ck+UY/M6a4spjstd3ZxlTQf/tymMqrN3RY8pfU9xlTM68nwDbYxRXH5Mz76f86yh39tFVx1RU3519TCW5PI5bLZirqoqXXnoJf/vb31C/fn0AwOXLlwEAlSpVstq2UqVKOHv2rN3XmTlzJqZNm2bTvm/fPvj6+gIAQkNDER0djdOnTyM1NVXbpmrVqqhatSpOnDihLd4DQFRUFMLCwnD48GFkZ2dr7TExMQgKCsK+ffusJk7Dhg3h6emJPXv2WPWhWbNmyM3NxcGDB7U2vV6P5s2bIyMjA8eOHdPavb290ahRI1y5cgWnTp3S2gMDA1G3bl1cvHgRFy5c0NoLGxMAdLyvIyK8IrT2rRlbceLmCfSo2ANBhiCtPfFqIpJzkjGg0gCrBeYVqSuQZcrCoMqDrMa0+PJi+Op90Se0j9aWp+Zh8eXFCPcKR1xwnNaebkzHitQVqOFdAwadAQMqDQAAJOckI/FqIh70fxCN/Rpr25+4eQJbM7aidWBr1PGpo7Xvu7EPe6/vLdUx5d9XjtpPZTn36tevj9zcXBw6dMhtxuTM+8lsNmPfvn1uNSZn3U9RUVG47777sG/fPrcZk7PvJ09PT+j1eqSmprrNmJx9P1lqijuNyVn3U2hoKDw9Pa1qiquPydn303333Qe9Xo9Tp065zZiceT9Z6ok7jcmZ95OHh4eWubuMydn3U9WqVaHX63Hw4EG3GZMz7yfL/HanMTnrfmrWrBlq1qxpVU9cfUzOvp/MZjNyc3OhKIrbjAlw3f2U//LdRVHU/MvvLm7EiBFYs2YNfv31V22Rd/v27WjTpg0uXryIKlWqaNs+88wzOH/+vM0lXQD7Z5hXq1YNaWlp2l1Uy9u3NbXn1na6M8wr6CogT82zaXfUGeaHRx3W2p39WzVLH4s79yzPs/THHcZUWLujx2R5jk6ng8FgcIsxOfN+UhQFeXl5UBQFiqK4xZiceT+pqgqz2QxPT0/tv119TIX13RnGZMnZUlPcYUzFaXfUmBRFQW5urvbf7jAmZ95PlrO2PDw8bGqKq44pfx+dbT8ZjUatnuh0OrcYk7PvJ1VVkZeXZ1NTXHlM9vruLGPKX1PMZrNbjKmwdkePyfLZx1JP3GFMzryf9Hq99hqWeuLqY3Lm/WQ5LvHw8ICiKG4xpqL67uxjyszMREhICDIyMrT13YK4zYL5Cy+8gNWrV+OXX35BzZo1tfZTp04hOjoae/fuRePGf5153KNHDwQFBSEhIaHI187MzERgYGCxAnVX0e9EO7oLVjwUDwyqPAiLLy/WFs0dLWlskqO7UGaMRiP27NmDZs2aaYstVHaYtyzmLYt5y2Pmspi3LOYti3nLY+aymLcs5i2Lecti3s6nJOu7ukIfdQGqqmLkyJFYuXIlNm7caLVYDgA1a9ZE5cqV8fPPP2ttubm52LJlC1q3bi3dXSIiIiIiIiIiIiJyUi7/FceIESPw5Zdf4ttvv4W/v792zfLAwEB4e3tDURSMHj0ab775JmrXro3atWvjzTffhI+PDwYOHOjg3hMRERERERERERGRs3D5BfOPPvoIABAbG2vV/tlnn2HIkCEAgPHjxyM7OxvPP/88rl27hpYtW+Knn36Cv7+/cG+JiIiIiIiIiIiIyFm5zTXMyxKvYe581zAHbl/H3FmuXw649zXMLTdnsNzwgcoW85bFvGUxb3nMXBbzlsW8ZTFvecxcFvOWxbxlMW9ZzNv5lKtrmFP5pECBr94XClh0pOTm5jq6C+UK85bFvGUxb3nMXBbzlsW8ZTFvecxcFvO27/r16xg/fjw6deqE0NBQKIqC+Ph4m+1UVcXHH3+Mpk2bIiAgACEhIWjXrh3WrFlj93XvzPuHH37AoEGD0KBBA3h4eBR7oXH9+vVQFAWKouDKlSslHl95wfkti3m7Li6Yk0syKAb0Ce0Dg+LyVxVyCSaTCQcPHoTJZHJ0V8oF5i2Lecti3vKYuSzmLYt5y2Le8pi5LOZdsLS0NCxYsAA5OTno2bNngdtNnToVzz77LFq0aIEVK1Zg0aJF8PLyQrdu3bBy5Uqrbe3lvWrVKuzcuRP16tVDo0aNitW3Gzdu4JlnnkF4ePhdja284PyWxbxdG1cbiYiIiIiIiIioQJGRkbh27Zp2Bvcnn3xid7tPP/0Uf/vb37T7zQHAo48+isqVKyMhIQG9e/cu9H0+/vhj6HS3z+0cOXIkfv/99yL79uqrr+K+++5D165dMWPGjBKMiojIPp5hTkREREREREREBbJc7qQoHh4eCAwMtGqrUKGC9qcolsXy4tq6dSsWLFiATz75BHq9vkTPJSIqCBfMyWU50w0/ywMefMhi3rKYtyzmLY+Zy2Lespi3LOYtj5nLYt73ZtSoUUhMTMTChQtx7do1XLp0CS+99BIyMjLw4osv2mx/L3lnZ2dj6NChGD16NJo0aXIv3S43OL9lMW/XxUuykEvKU/Ow+PJiR3ej3DAYDGjevLmju1FuMG9ZzFsW85bHzGUxb1nMWxbzlsfMZTHvezd69Gh4e3tjxIgRGDZsGAAgODgY33//Pdq0aWO17b3mPXnyZJhMJkybNu2e+lxecH7LYt6ujWeYk0tSoCDCKwIKinfHbLo3qqoiPT0dqqo6uivlAvOWxbxlMW95zFwW85bFvGUxb3nMXBbzvnefffYZRo0ahZEjR2L9+vX48ccf0alTJ/To0QPr1q2z2vZe8v7tt9/w7rvv4r///S+8vb1Lq/tujfNbFvN2bVwwJ5dkUAyIC46DQeEvSUgwmUw4duwY7+4shHnLYt6ymLc8Zi6Lecti3rKYtzxmLot535tr165pZ5a/8847eOSRR9ClSxcsXboUzZs3x3PPPWe1/b3k/fTTT6N3795o1qwZ0tPTkZ6ejlu3bgEAMjMzcf369VIZkzvh/JbFvF0bVxuJiIiIiIiIiOieHD9+HNnZ2XYvQ9GsWTNs2bIFN27cgJ+f3z2/15EjR3DkyBEsW7bM5rHo6Gg0atQI+/fvv+f3IaLyiQvmRERERERERER0T8LDwwEAO3fuxODBg7V2VVWxc+dO3HffffD19S2V99q0aZNN26JFi5CQkIDVq1cjIiKiVN6HiMonLpiTS1KhIt2YDhW8FpQERVHg7e0NReE14yUwb1nMWxbzlsfMZTFvWcxbFvOWx8xlMe/CrV27FllZWdrlTv744w8sX74cAPDYY4+hevXq6N27NxYsWAAvLy889thjyMnJQUJCArZt24bp06dbZVuhQgU0bdoU27dv19rOnj2L3bt3AwCSkpIAQHuPGjVqoFmzZgCA2NhYm/5t3rwZANCmTRtUrFixdAfvBji/ZTFv16aovPp8kTIzMxEYGIiMjAwEBAQ4ujsOEf1OtKO74PSSxiY5ugtEREREREREZaJGjRo4e/as3cdOnz6NGjVq4NatW5g/fz4+//xznD59Gh4eHqhTpw5GjhyJgQMHWi0eKoqCdu3aaQvdwO2zxJ966im77zF48GAsWrSowP7Fx8dj2rRpSE1N5YI5EdkoyfouF8yLgQvmzrdgroMOtXxq4c+bf8IMs6O7A8C9F8zNZjOuXLmCihUrQqfjvYLLGvOWxbxlMW95zFwW85bFvGUxb3nMXBbzlsW8ZTFvWczb+ZRkfZd7jFySXtGjbWBb6BW9o7tSLpjNZpw6dQpms3N8OeHumLcs5i2Lectj5rKYtyzmLYt5y2Pmspi3LOYti3nLYt6ujQvmRERERERERERERETgTT+JiIiIiIiIiNyes11q1UPxwKDKgzBk2xDkqXmO7o7GnS+3SkTFwzPMySWpUJGckwwVvAS/BEVREBgYyLs7C2Hespi3LOYtj5nLYt6ymLcs5i2Pmcti3rL4uV4W57cs5u3aeNPPYuBNP53vm2hnxG+hiYiIiIiIyFnxc33x8LM9kXviTT/J7emgQxP/JtBxCoswm824cOECb1YhhHnLYt6ymLc8Zi6Lecti3rKYtzxmLot5y+Lnelmc37KYt2tjVSKXpFf0aOzXGHpF7+iulAss9LKYtyzmLYt5y2Pmspi3LOYti3nLY+aymLcsfq6Xxfkti3m7Ni6YExERERERERERERGBC+ZERERERERERERERAC4YE4uyqyaceLmCZhV/mqLBJ1Oh9DQUOh0LBkSmLcs5i2Lectj5rKYtyzmLYt5y2Pmspi3LH6ul8X5LYt5uzbuNXJJJpiwNWMrTDA5uivlgk6nQ3R0NAu9EOYti3kX7Pr16xg/fjw6deqE0NBQKIqC+Ph4u9vm5eVh7ty5aNCgAby9vREUFITWrVtj+/btVtsVlPf69evx0EMPwcfHBxUrVsSQIUOQkpJi8z4nTpxAnz59cN9998HHxwctW7bEd999V2pjdkec47KYtyzmLYt5y2Pmspi3LH6ul8X5LYt5uzbuNXJJeujRNrAt9ODNQSSYzWYkJSXxZhVCmLcs5l2wtLQ0LFiwADk5OejZs2eB25lMJvTq1Quvv/46BgwYgLVr12LJkiWIi4tDVlaW1bb28t6yZQu6dOmCSpUq4dtvv8W8efOwfv16PPLII8jJydG2O3PmDB566CEcP34c//nPf7Bs2TKEhoaiZ8+eWLFiRamP311wjsti3rKYtyzmLY+Zy2Lesvi5Xhbntyzm7doMju4A0d3QKTrU8amDnZk7YVL5bXRZM5vNSE1NRWRkJL8dFcC8ZTHvgkVGRuLatWtQFAVXrlzBJ598Yne7999/H2vXrsW2bdvQqlUrrb1r164229rLe9y4cahTpw6WL18Og+H2oUnNmjXRpk0bfPrppxg+fDgA4K233sLNmzexbt06REREAADi4uLQoEEDjBkzBr169eI+tINzXBbzlsW8ZTFvecxcFvOWxc/1sji/ZTFv18Y9RkRERHYpigJFUYrcbt68eXj44YetFsuLKzk5Gbt378aTTz6pLZYDQOvWrVGnTh2sWrVKa9u2bRsaNWqkLZYDgF6vR5cuXXD+/Hn89ttvJX5/IiIiIiIiovy4YE5ERER37fz58zhz5gwaNGiACRMmoFKlSjAYDHjggQeQkJBQ5PMPHz4MAGjYsKHNYw0bNtQeB4Dc3Fx4eXnZbGdpO3jw4N0Og4iIiIiIiAgAL8lCLsqkmrDvxj7+2pYQnU6HqlWr8teIhDBvWcz73iQnJwMAEhISULVqVcyfPx+BgYH4+OOPMWTIEOTm5uKZZ57Rtr8z77S0NABAcHCwzWsHBwdrjwNAvXr1sHnzZty4cQN+fn5a+6+//mr1WmSNc1wW85bFvGUxb3nMXBbzlsXP9bI4v2Uxb9fGvUYuyQwz9l7fCzN48wQJLPSymLcs5n1vLDexuXXrFn788Uf069cPnTp1wjfffIMmTZrg9ddft9q+oLwLuvRL/vaRI0ciIyMDgwYNwqlTp/C///0PkydPxvbt27XXJluc47KYtyzmLYt5y2Pmspi3LH6ul8X5LYt5uzbuNXJJBsWAuOA4GBT+koQEk8mEo0ePwmTiN/8SmLcs5n1vQkJCAAAxMTGIjIzU2hVFQefOnXHhwgWkpKRo7XfmbXm+vbPDr169anXm+SOPPILPPvsMv/zyC6Kjo1G5cmWsXLkS06dPBwCra5vTXzjHZTFvWcxbFvOWx8xlMW9Z/Fwvi/NbFvN2bVwwJ5ekQEGEVwQUFH0zOrp3qqoiIyMDqqo6uivlAvOWxbzvTXR0NHx8fOw+Zsk0/1kVd+Zdv359AMChQ4dsnn/o0CHtcYvBgwfj8uXL+OOPP3Dy5EkcOXIEwO0F+rZt2977gNwQ57gs5i2Lecti3vKYuSzmLYuf62Vxfsti3q6NC+ZERER01wwGA3r06IGjR4/izJkzWruqqkhMTER0dDQqVqxY4PMjIiLQokULfPHFF1ZnX+zcuRPHjx9H79697b5n3bp1UatWLWRkZGDBggXo0aOH1RnuRERERERERHeDv/dCREREBVq7di2ysrJw/fp1AMAff/yB5cuXAwAee+wx+Pj4YPr06Vi7di3i4uIQHx+PgIAAfPLJJzhw4AC++eYbq9erUKECHnzwQezcuVNrmzVrFh599FH069cPzz//PFJSUvDqq6+ifv36eOqpp7TtUlJSMGfOHLRp0wb+/v44duwYZs+eDZ1Ohw8++EAgDSIiIiIiInJ3XDAnl2RSTdiasZV30xai0+kQFRXFm1UIYd6ymHfhhg8fjrNnz2p/X7ZsGZYtWwYAOH36NGrUqIHo6Ghs3boVr776Kp599lnk5eXhwQcfxHfffYdu3bpZvZ7JZIKXl5dV3rGxsfjxxx8xZcoUdO/eHT4+PujWrRvefvtteHl5adsZDAbs378fn332GdLT01GlShX06NEDU6ZMKfQs9vKOc1wW85bFvGUxb3nMXBbzlsXP9bI4v2Uxb9emqLyYTpEyMzMRGBiIjIwMBAQEOLo7DhH9TrSju+D0ksYmOboLRERERERERHbxc33x8LM9kXsqyfouv+Ygl2RQDOgT2od30xZiMplw4MAB3t1ZCPOWxbxlMW95zFwW85bFvGUxb3nMXBbzlsXP9bI4v2Uxb9fGBXNySQoUBBmCeDdtIaqqIjs7m3d3FsK8ZTFvWcxbHjOXxbxlMW9ZzFseM5fFvGXxc70szm9ZzNu18Ws8IiIiN+dMv37roXhgUOVBGLJtCPLUPEd3R8NfvSUiIiIiIiKAZ5gTEREREREREREREQHggjm5KKNqROLVRBhVo6O7Ui7o9XrExMRAr9c7uivlAvOWxbxlsX7L4xyXxbxlMW9ZzFseM5fFvGXxuFAW57cs5u3aeEkWckkqVCTnJDu6G+WGoigICgpydDfKDeYti3nLYv2Wxzkui3nLYt6ymLc8Zi6LecvicaEszm9ZzNu18QxzckmWa+B6KB6O7kq5YDQasXv3bhiN/OZfAvOWxbxlsX7L4xyXxbxlMW9ZzFseM5fFvGXxuFAW57cs5u3auGBOLov/qMoymUyO7kK5wrxlMW9ZrN/yOMdlMW9ZzFsW85bHzGUxb1k8LpTF+S2LebsuLpgTEREREREREREREYEL5kREREREREREREREANxgwfyXX35B9+7dER4eDkVRsHr1aqvHhwwZAkVRrP60atXKMZ2lUmNUjViRuoJ30xai1+vRsGFD3t1ZCPOWxbxlsX7L4xyXxbxlMW9ZzFseM5fFvGXxuFAW57cs5u3aXH7BPCsrC40aNcL8+fML3CYuLg6XLl3S/vz444+CPaSyoEJFlikLKlRHd6Xc8PT0dHQXyhXmLYt5y2H9dgzOcVnMWxbzlsW85TFzWcxbDo8L5XF+y2LersvlF8y7dOmCGTNmoHfv3gVu4+XlhcqVK2t/goODBXtIZYF305ZlMpmwZ88e3rBCCPOWxbxlsX7L4xyXxbxlMW9ZzFseM5fFvGXxuFAW57cs5u3aXH7BvDg2b96MsLAw1KlTB8888wxSUlIc3SUiIiIiIiIiIiIicjIGR3egrHXp0gX9+vVDZGQkTp8+jcmTJ6NDhw74/fff4eXlZfc5OTk5yMnJ0f6emZkJADAajTAab19bS6fTQafTwWw2w2w2a9ta2k0mE1RVLbJdr9dDURTtdfO3A7D5JqqgdoPBAFVVrdoVRYFer7fpY0HthY0JAAyKAQoUrd2kmmCG2abdqBqhQrX5lrig9jw1DwoUGBRDke0qVBhVI3TQQYGivVb+dr3y1/WhzKoZJpighx465a/vhwrq+72MKf8+dNR+Kqu5Z3muqqo227vqmAprd/SYLI+ZTCa3GZMz7ycANq/j6mO6cz95KB5lUvfyK24t91A8tPe5s2Y7tJbfkTvgPj9Pd9YUdxhTcdodNSbAtqa4+piceT/l75e7jCl/H51xTJb/d6cxFdV3R47Jcgx+Z01x5THZ67uzjKmg/3blMeVvd9Rn94KO9yzHhR6Kh+hn96L6bjQa3fLnCbA9RnH1MTnzfsq/juLOnzVcaUx3jqMwDlkwT0pKQnx8PDZs2IC0tDRUrFgRHTt2xJQpUxAdHV2q79W/f3/tv+vXr49mzZohMjISa9asKfAyLjNnzsS0adNs2vft2wdfX18AQGhoKKKjo3H69GmkpqZq21StWhVVq1bFiRMnkJGRobVHRUUhLCwMhw8fRnZ2ttYeExODoKAg7Nu3z2riNGzYEJ6entizZ49VH5o1a4bc3FwcPHhQa9Pr9WjevDkyMjJw7Ngxrd3b2xuNGjXClStXcOrUKa09MDAQdevWxcWLF3HhwgWtvbAxAUDH+zoiwitCa9+asRUnbp5Aj4o9EGQI0toTryYiOScZAyoNsPrHaEXqCmSZsjCo8iCrMS2+vBi+el/0Ce2jteWpeVh8eTHCvcIRFxyntacb07EidQWivaNRrUI1DKw8EKqqIjknGYlXE/Gg/4No7NdY2/7EzRPYmrEVrQNbo45PHa1934192Ht9b6mOKf++ctR+Kqu5Zykut27dwpEjR9xiTIDz7qebN28iPT0de/fuRd26dd1iTM68nyIjI5GdnY29e/dCURS3GNOd+2lQ5UFlUvfyK24tVxQFlT0rAwBq+dRC28C22vaOrOWWX5nMz11+nlRV1WpK8+bN3WJMzryfgoODcf36daua4upjcub9pKqq1i93GRPgvPvp6NGjWj3x8fFxizE5+37S6/Va5paa4upjcub9pKoqcnNzAcBtxgT8tZ8c8dm9sOO9Jv5NUM3r9uf641nHxT67FzWmffv2ueXPU+PGjWE0Gq3qiauPyZn3k+UY/NatW/D29naLMbn6fsrKykJxKWr+5XcBx44dw0MPPYRbt26hQ4cOCA8Px8WLF7Fx40b4+Phg27ZtiImJuavXVhQFq1atQs+ePQvdrnbt2hg2bBheeeUVu4/bO8O8WrVqSEtLQ0BAAIDy921N7bm1HX5WImD9bXQFXQXkqXk27Y46w/zwqMNau7N/q2bpY3HnnuV5lv64w5gKa3f0mCzP0el0MBgMbjEmZ95PiqIgLy8PiqJoB46uPqY791P9efWd5gxz4PZvLGWbs53qDPOTL590+H7K316ac09VVaua4g5jKk67o8akKApyc3O1/3aHMTnzfrKcteXh4aHNdVcfU/4+Ott+MhqNWj3R6XRuMSZn30+qqiIvL8+mprjymOz13VnGlL+mmM1mtxhT/vb759zvVGeY6xU9PBQP5Kl5TnWG+eFRh93y50mv12uvYaknrj4mZ95PluMSDw8PKIriFmMqqu/OPqbMzEyEhIQgIyNDW98tiPiCee/evXHw4EFs3rxZO3MZAC5cuIAOHTqgQYMGWLFixV29dnEWzNPS0hAREYEFCxZg0KBBBW6XX2ZmJgIDA4sVqLuKfqd0z/y/VwoUBBoCkWHMcJo7aieNTXJ0F8qM5ewtb29v7R9WKjvMW1Z5yNuZargz1m+ANZxKD/OWxbxlMW95zFyWu+ftTMeEAI8Lpbn7/HY2zNv5lGR9V/ymn1u2bMG0adOsFsuB26fQT5kyBZs2bSrR6924cQP79+/H/v37Adz+Vcz9+/fj3LlzuHHjBsaOHYsdO3bgzJkz2Lx5M7p3746KFSuiV69epTUkcgCDYkCf0D4232xT2TCZTDh48KDNt4RUNpi3LOYti/VbHue4LOYti3nLYt7ymLks5i2Lx4WyOL9lMW/XJl6Vbt68iZCQELuPVaxY0epaOcWxZ88etG/fXvv7Sy+9BAAYPHgwPvroIxw6dAiLFy9Geno6qlSpgvbt2+Prr7+Gv7//3Q+CiIiIiIiIiIiIiNyO+IL5/fffjyVLliAuLs7msaVLl5b4+uWxsbEo7Koy69atK3EfiYiIiIiIiIiIiKj8EV8wf/HFFzFs2DBkZGRg8ODBqFKlCi5duoQvvvgC3333HT755BPpLpGLstzwk2RYbt5AMpi3LOYti/VbHue4LOYti3nLYt7ymLks5i2Lx4WyOL9lMW/XJX7TTwCYOXMmZsyYgVu3bgG4fSF8b29vTJ48Ga+++qp0d4rEm346381BnJG73hiEiFwfa3jRWMOJiIjI3fGYsHh4XEjknpz6pp8A8Nprr+HixYtYs2YNFi9ejB9//BEXL150ysVyck4KFER4RUAB7zQsQVVVpKenF3r5Iyo9zFsW85bF+i2Pc1wW85bFvGUxb3nMXBbzlsXjQlmc37KYt2tzyII5AAQGBiIuLg7/+Mc/EBcXh8DAQEd1hVyQQTEgLjiOd9MWYjKZcOzYMd7dWQjzlsW8ZbF+y+Mcl8W8ZTFvWcxbHjOXxbxl8bhQFue3LObt2kSq0rlz51ClShV4eHjg3LlzRW5fvXp1gV4REREREREREREREf1FZMG8Zs2a2LFjB1q0aIEaNWpAUQr/dRt++0JERERERERERERE0kQWzD/99FNER0dr/13UgjlRUVSoSDemQwWvBSVBURR4e3vzZ1cI85bFvGWxfsvjHJfFvGUxb1nMWx4zl8W8ZfG4UBbntyzm7doUlVefL1JJ7qLqrng37aLxTtpE5KxYw4vGGk5ERETujseExcPjQiL3VJL1XfGbfj799NM4ffq03cfOnj2Lp59+WrhH5Ip00KGOTx3oHHff2nLFbDYjJSUFZrPZ0V0pF5i3LOYti/VbHue4LOYti3nLYt7ymLks5i2Lx4WyOL9lMW/XJl6VFi1ahNTUVLuPXblyBQkJCcI9IlekV/RoG9gWekXv6K6UC2azGadOnWKhF8K8ZTFvWazf8jjHZTFvWcxbFvOWx8xlMW9ZPC6Uxfkti3m7Nqf6Gu/q1avw8vJydDeIiIiIiIiIiIiIqBwSuennL7/8gs2bN2t//+STT5CYmGi1TXZ2Nr799lvUq1dPoktERERERERERERERFZEFsw3bdqEadOmAbh9l9hPPvnE7naRkZH44IMPJLpELk6FiuScZN5NW4iiKAgMDOTdnYUwb1nMWxbrtzzOcVnMWxbzlsW85TFzWcxbFo8LZXF+y2Lerk1RVbXMK1N2djZu3rwJVVURFhaGdevWoUmTJlbbeHl5wc/Pr6y7cldKchdVd8W7aReNd9ImImfFGl401nAiIiJydzwmLB4eFxK5p5Ks74pcw9zb2xshISGoWLEiTp8+jdjYWISEhFj9cdbFcnJOOujQxL8J76YtxGw248KFC7xZhRDmLYt5y2L9lsc5Lot5y2Lespi3PGYui3nL4nGhLM5vWczbtYlXpcjISHh4eEi/LbkZvaJHY7/GvJu2EBZ6WcxbFvOWxfotj3NcFvOWxbxlMW95zFwW85bF40JZnN+ymLdrE7mG+Z1++eUXvPfeezh69Ciys7OtHlMUBUlJ/PUXIiIiIiIiIiIiIpIlfob5r7/+ikceeQQZGRk4evQoYmJiEBERgXPnzsFgMODhhx+W7hIRERERERERERERkfyC+dSpU/HUU08hMTERADBjxgxs3boVe/fuxY0bN9C7d2/pLpELMqtmnLh5AmaVv9oiQafTITQ0FDodry0ngXnLYt6yWL/lcY7LYt6ymLcs5i2Pmcti3rJ4XCiL81sW83Zt4nvt8OHD6NWrFxRFAQCYTCYAQMOGDTF58mS8/vrr0l0iF2SCCVsztsIEk6O7Ui7odDpER0ez0Ath3rKYtyzWb3mc47KYtyzmLYt5y2Pmspi3LB4XyuL8lsW8XZv4Xrt58yb8/Pyg0+ng5eWFK1euaI/FxMTgjz/+kO4SuSA99Ggb2BZ68OYgEsxmM5KSknizCiHMWxbzlsX6LY9zXBbzlsW8ZTFvecxcFvOWxeNCWZzfspi3axNfMK9evTr+97//AQDq1auHNWvWaI9t2bIFISEh0l0iF6RTdKjjUwc6hd/USTCbzUhNTWWhF8K8ZTFvWazf8jjHZTFvWcxbFvOWx8xlMW9ZPC6Uxfkti3m7NoP0G8bGxmLz5s3o27cvnnnmGTz//PM4evQovLy88NNPP+Hll1+W7hIRERERERERERERkfyC+bRp03D16lUAwHPPPYebN29iyZIlUBQFkyZNwsSJE6W7REREREREREREREQkv2BesWJFVKxYUfv7Sy+9hJdeekm6G+TiTKoJ+27sg0nlzUEk6HQ6VK1alTerEMK8ZTFvWazf8jjHZTFvWcxbFvOWx8xlMW9ZPC6Uxfkti3m7Nqfaa9u3b0fHjh0d3Q1yAWaYsff6XpjBa0FJYKGXxbxlMW9ZrN/yOMdlMW9ZzFsW85bHzGUxb1k8LpTF+S2Lebs2sb1mMpmwZcsWfPPNN9i/f7/VY7///ju6dOmCtm3bYvfu3VJdIhdmUAyIC46DQRH/JYlyyWQy4ejRozCZ+M2/BOYti3nLYv2Wxzkui3nLYt6ymLc8Zi6LecvicaEszm9ZzNu1iSyY/+9//8ODDz6IDh06YMCAAWjatCmefPJJmEwmDB8+HC1btsSGDRswfPhw/PnnnxJdIhenQEGEVwQUKI7uSrmgqioyMjKgqqqju1IuMG9ZzFsW67c8znFZzFsW85bFvOUxc1nMWxaPC2Vxfsti3q5N5Gu8yZMn4+jRoxgyZAiaNWuGU6dO4T//+Q86duyILVu24NFHH8UHH3yAWrVqSXSHiIiIiIiIiIiIiMiGyIL5unXrMHbsWLz11ltaW4sWLdC/f3/07dsX33zzjUQ3iIiIiIiIiIiIiIgKJHJJlkuXLqFTp05WbZa/P/vssxJdIDdjUk3YmrGVd9MWotPpEBUVxZtVCGHespi3LNZveZzjspi3LOYti3nLY+aymLcsHhfK4vyWxbxdm8gZ5kajEX5+flZtlr8HBwdLdIHcjBlmnLh5wtHdKDd0Oh3CwsIc3Y1yg3nLYt6yWL/lcY7LYt6ymLcs5i2Pmcti3rJ4XCiL81sW83ZtYl9zZGZm4urVq9qftLQ0u+1Xr16V6hK5MINiQJ/QPrybthCTyYQDBw7w7s5CmLcs5i2L9Vse57gs5i2Lecti3vKYuSzmLYvHhbI4v2Uxb9cmVpU6d+5st/2RRx6xaeNkoqIoUBBkCOLdtIWoqors7Gze3VkI85bFvGWxfsvjHJfFvGUxb1nMWx4zl8W8ZfG4UBbntyzm7dpEFsynTp0q8TZERERERERERERERHeNC+ZERERERERERERERBC8hjlRaTKqRiReTYRRNTq6K+WCXq9HTEwM9Hq9o7tSLjBvWcxbFuu3PM5xWcxbFvOWxbzlMXNZzFsWjwtlcX7LYt6ujXdWIJekQkVyTrKju1FuKIqCoKAgR3ej3GDespi3LNZveZzjspi3LOYti3nLY+aymLcsHhfK4vyWxbxdG88wJ5fkoXhgUOVB8FA8HN2VcsFoNGL37t0wGvnNvwTmLYt5y2L9lsc5Lot5l9xvv/2Gzp07w9/fH35+fmjfvj22bdtWrOcajUZ89NFH6NixI8LCwuDn54eGDRvivffeg8lk0rY7c+YMFEUp8E9cXFxZDc+tcH7LY+aymLcsHhfK4vyWxbxdGxfMyWXxH1VZ+T90Utlj3rKYtyzWb3mc47KYd/Ht3r0bDz/8MLKzs/H555/j888/x61bt/DII49gx44dRT5/w4YNGDlyJIxGIz7++GOsXr0asbGxGDVqFF566SVtuypVqmDHjh02f1555RUAQK9evcpsjO6G81seM5fFvGXxuFAW57cs5u26eEkWIiIiIiJyiMmTJyMoKAiJiYnw8fEBAHTs2BFRUVEYO3ZskWeaJyQkwGAw4Ntvv0VgYKD2/OPHj2PRokWYN28eAMDLywutWrWyef5rr70GHx8fDBgwoJRHRkRERESuimeYExERERGRQ2zbtg2xsbHaYjkA+Pv74+GHH8b27dtx6dKlQp/v4eEBg8EAb29vq/agoCBUqFCh0OcmJSVhy5YtePzxxxEQEHD3gyAiIiIit+KQBfO8vDwsXLgQAwcOROfOnfGPf/wDn332GfLy8hzRHXJBRtWIFakreDdtIXq9Hg0bNuTdnYUwb1nMWxbrtzzOcVnMu2Ryc3Ph5eVl025pO3ToUKHPHz58OEwmE8aMGYOLFy8iPT0dn3/+OVatWoXx48cX+txPP/0Uqqpi2LBhdz+AcobzWx4zl8W8ZfG4UBbntyzm7drEL8mSkZGBRx55BHv37oWvry8qV66M7du3Y+nSpfjwww+xYcMGnuFBRVKhIsuUBRWqo7tSbnh6ejq6C+UK85bFvOWwfjsG57gs5l189erVw86dO2E2m6HT3T6Xx2g0YteuXQCAtLS0Qp/fsmVL/PTTTxgwYAA+/PBDALc/oM6cORMvv/xygc8zmUxISEhATEwM2rRpU0qjKR84v+Uxc1nMWw6PC+Vxfsti3q5L/AzziRMn4vjx4/j6669x/fp1nDx5EtevX8c333yD48ePY+LEidJdIhfEu2nLMplM2LNnD29YIYR5y2Lesli/5XGOy2LeJfPCCy/gxIkTGDlyJJKTk3H+/Hk899xzOHv2LABoi+gF+e2339CzZ080adIE33//PTZu3IjXXnsNkyZNwvTp0wt8XmJiIpKTkzF06NBSHY+74/yWx8xlMW9ZPC6Uxfkti3m7NvEzzFevXo3XX38d/fr1s2rv27cvzp07h7lz5+L999+X7hYREREREQl7+umnkZqaihkzZuCjjz4CADz00EMYO3YsZs2ahYiIiEKf/+KLLyI4OBjLly/XLuPSvn176HQ6xMfH4x//+AeioqJsnrdw4UJ4eHhg0KBBpT8oIiIiInJp4meYp6amomHDhnYfa9SoEa5cuSLcIyIiIiIicpRXXnkFV65cwaFDh3DmzBls374d165dg6+vL5o2bVrocw8cOICYmBib64M2b94cZrMZR48etXlOSkoKfvjhB/z9739HWFhYqY6FiIiIiFyf+IJ5REQEfv31V7uPbdu2DeHh4SV6vV9++QXdu3dHeHg4FEXB6tWrrR5XVRXx8fEIDw+Ht7c3YmNjceTIkbvtPhERERERlTIvLy/Ur18fkZGROHfuHL7++ms888wz8Pb2LvR54eHhOHr0qM2vO+/YsQMAULVqVZvnLF68GHl5ebwcCxERERHZJb5g3r9/f7z55puYO3eudhOftLQ0zJs3D2+++SaeeOKJEr1eVlYWGjVqhPnz59t9fPbs2Zg7dy7mz5+P3bt3o3Llynj00Udx/fr1ex4LOU6emofFlxcjT81zdFfKBb1ej2bNmvHuzkKYtyzmLYv1Wx7nuCzmXTKHDx/GtGnTsGbNGqxfvx5z5sxB06ZNUbt2bZtrkA8dOhQGg0G7vjkAjB49GqdOnUKvXr3w7bff4ueff8arr76K2bNno2PHjmjUqJHNey5cuBDVqlVD586dy3x87obzWx4zl8W8ZfG4UBbntyzm7drEr2EeHx+Pffv2YezYsRg3bhwMBgOMRiNUVUXnzp0RHx9fotfr0qULunTpYvcxVVXx7rvvYuLEiejduzcAICEhAZUqVcKXX36Jf/3rX/c6HHIQBQp89b7IMGbwjtpCcnNzizzLi0oP85bFvOWwfjsG57gs5l18np6e2LhxI9577z3cuHED1atXx3PPPYdXX30Vvr6+VtuaTCaYTCao6l+144UXXkBoaCg+/PBDDBs2DNnZ2ahRowamTp2KMWPG2Lzf9u3bcezYMUyZMqXIG4qSfZzf8pi5LOYth8eF8ji/ZTFv1yV+lOjl5YXExESsXbsW48aNw6BBgzBu3DisW7cOa9euhaenZ6m91+nTp3H58mV06tTJ6v3btWuH7du3l9r7kDyDYkCf0D4wKOLf+ZRLJpMJBw8e5N2dhTBvWcxbFuu3PM5xWcy7ZOrUqYMtW7YgLS0NOTk5OHnyJKZPn26zWA4AixYtgqqqqFGjhtZmMplQs2ZNbNq0Campqbhx4wYOHz6MSZMm2X2N1q1bQ1VVTJs2rSyH5bY4v+Uxc1nMWxaPC2Vxfsti3q7NYVWpc+fOZf5rkJcvXwYAVKpUyaq9UqVKVr/KeaecnBzk5ORof8/MzAQAGI1GGI1GAIBOp4NOp4PZbIbZbNa2tbTfefZLQe16vR6Komivm78dgM0PVkHtBoMBqqpatSuKAr1eb9PHgtoLGxNw+x8zBYrWblJNMMNs025UjVChwkPxsOpjQe15ah4UKDb/SNprV6HCqBqhgw4KFO218rfrlb9+3cWsmmGCCXrooVP++n6ooL7fy5jy70NH7aeymnuW56qqarO9q46psHZHj8nymMlkcpsxOfN+AmDzOq4+pjv3k4fiUSZ1L7/i1nIPxUN7nztrtkNr+R25A+7z83RnTXGHMRWn3VFjAmxriquPyZn3U/5+ucuY8vfRGcdk+X93GlNRfXfkmCzH4HfWFFcek72+O8uYCvpvVx5T/nZHfXYv6HjPclzooXiIfnYvqu9Go9Etf54A22MUVx+TM++n/Oso7vxZw5XGdOc4CuPQr/FSU1ORnZ1t0169evVSfR9FUaz+rqqqTVt+M2fOtHvWyb59+7QzVUJDQxEdHY3Tp08jNTVV26Zq1aqoWrUqTpw4gYyMDK09KioKYWFhOHz4sNWYY2JiEBQUhH379llNnIYNG8LT0xN79uyx6kOzZs2Qm5uLgwcPam16vR7NmzdHRkYGjh07prV7e3ujUaNGuHLlCk6dOqW1BwYGom7durh48SIuXLigtRc2JgDoeF9HRHhFaO1bM7bixM0T6FGxB4IMQVp74tVEJOckY0ClAVb/GK1IXYEsUxYGVR5kNabFlxfDV++LPqF9tDbLtczCvcIRFxyntacb07EidQWivaNRrUI1DKw8EKqqIjknGYlXE/Gg/4No7NdY2/7EzRPYmrEVrQNbo45PHa1934192Ht9b6mOKf++ctR+Kqu5Zykut27dsrppriuPCXDe/XTz5k2kp6dj7969qFu3rluMyZn3U2RkJLKzs7F3717t3wZXH9Od+2lQ5UFlUvfyK24tVxQFlT0rAwBq+dRC28C22vaOrOUmk8nh+wkom7mnqqpWU5o3b+4WY3Lm/RQcHIzr169b1RRXH5Mz7ydVVbV+ucuYAOfdT0ePHtXqiY+Pj1uMydn3k16v1zK31BRXH5Mz7ydVVZGbmwsAbjMm4K/95IjP7oUd7zXxb4JqXrc/1x/POi722b2oMe3bt88tf54aN24Mo9FoVU9cfUzOvJ8sx+C3bt2Ct7e3W4zJ1fdTVlYWiktR8y+/C7h+/TrGjBmDpUuX4tatW3a3sfctWHEoioJVq1ahZ8+eAIBTp04hOjoae/fuRePGf33w7tGjB4KCgpCQkGD3deydYV6tWjWkpaUhICAAQPn7tqb23NoOPysR+OvbaC/FC09UegLLUpYhT81zijPMD486rLU7+7dqlj6W5AzzAwcOoHHjxjZfNrnqmAprd/SYjEYj9u/fjwcffBCenp5uMSZn3k9msxl79+5Fo0aNtPdy9THduZ/qz6vvVGeY9wvrhy//9yVMqslpzjA/+fJJh++n/O2lOfdMJpNVTXGHMRWn3VFjsldTXH1M+dvrz6sPwPFnJVra89cUs2p2+FmJlvY/X/7TLX+ecnNztXpiMBjcYkzOvp8si1sPPvigVU1x5THZ67uzjMnyuadJkyZQFMUtxpS//f459zvVGeYVdBXQL6wflqUsQ445x2nOMD886rBb/jwBsDlGcfUxOfN+shyDN2nSBAaDwS3GVFTfnX1MmZmZCAkJQUZGhra+WxDxBfOhQ4fiyy+/xNChQ9GwYUN4eXnZbDN48OC7eu07F8xVVUV4eDjGjBmD8ePHA7h9wf2wsDDMmjWr2Df9zMzMRGBgYLECdVfR70Q7ugtOL2lskqO7QERkF2t40VjDiYqH9aR4WFOIyBmxhhcPaziReyrJ+q74JVnWrFmDt956C6NGjSqV17tx4wb+/PNP7e+nT5/G/v37ERwcjOrVq2P06NF48803Ubt2bdSuXRtvvvkmfHx8MHDgwFJ5f3IMBQrCvcJxMeci76YtQFVVZGRkIDAwsNDLGVHpYN6ymLcs1m95nOOymLcs1hRZnN/ymLks5i2LNVwW57cs5u3adEVvUrpu3bqFBg0alNrr7dmzB40bN9YuufLSSy+hcePGmDJlCgBg/PjxGD16NJ5//nk0a9YMycnJ+Omnn+Dv719qfSB5BsWAuOA43k1biMlkwrFjx+76cklUMsxbFvOWxfotj3NcFvOWxZoii/NbHjOXxbxlsYbL4vyWxbxdm3hVeuyxx7B161Z06NChVF4vNjYWhV1VRlEUxMfHIz4+vlTej4iIiIiIiIiIiIjck/iC+aRJk9C3b1/4+/uje/fuCAkJsdkmODhYultEREREREREREREVM6JL5jXr18fADBu3DiMGzfO7jb8dQUqigoV6cZ0XudMiKIo8Pb25nW3hDBvWcxbFuu3PM5xWcxbFmuKLM5vecxcFvOWxRoui/NbFvN2bYpa2PVMykB8fHyRk2Xq1KlCvSmektxF1V3xbtpF4520ichZsYYXjTWcqHhYT4qHNYWInBFrePGwhhO5p5Ks74qfYc5riVNp0EGHWj618OfNP2GG2dHdcXtmsxlXrlxBxYoVodOJ3yu43GHespi3LNZveZzjspi3LNYUWZzf8pi5LOYtizVcFue3LObt2rjHyCXpFT3aBraFXtE7uivlgtlsxqlTp2A286RE03IAAQAASURBVCBGAvOWxbxlsX7L4xyXxbxlsabI4vyWx8xlMW9ZrOGyOL9lMW/XJnKG+eLFi9G1a1eEhIRg8eLFRW4/aNAggV4REREREREREREREf1FZMF8yJAh2LlzJ0JCQjBkyJBCt1UUhQvmRERERERERERERCROZMH89OnTqFKlivbfRPdKhYrknGTeTVuIoigIDAzk3Z2FMG9ZzFsW67c8znFZzFsWa4oszm95zFwW85bFGi6L81sW83ZtiqqqrExFKMldVN0V76ZdNN5Jm4icFWt40VjDiYqH9aR4WFOIyBmxhhcPaziReyrJ+q74TT+joqJw4MABu48dPnwYUVFRwj0iV6SDDk38m0DH+9aKMJvNuHDhAm9WIYR5y2Lesli/5XGOy2LeslhTZHF+y2Pmspi3LNZwWZzfspi3axOvSmfOnEFOTo7dx27duoWzZ88K94hckV7Ro7FfY95NWwgLvSzmLYt5y2L9lsc5Lot5y2JNkcX5LY+Zy2LesljDZXF+y2Lers0hX+MVdP2eU6dOwd/fX7g3RERERERERERERERCN/1MSEhAQkKC9vfhw4fbXCsmOzsbBw4cQLt27SS6RERERERERERERERkRWTB/ObNm0hNTdX+np6ebnNZFi8vL/Tv3x/Tpk2T6BK5OLNqxombJ2BW+astEnQ6HUJDQ6HT8dpyEpi3LOYti/VbHue4LOYtizVFFue3PGYui3nLYg2Xxfkti3m7NkVVVVXyDWvWrInVq1ejUaNGkm97T0pyF1V3xbtpF4130iYiZ8UaXjTWcKLiYT0pHtYUInJGrOHFwxpO5J5Ksr4r+jVHdnY2WrVqhevXr0u+LbkhPfRoG9gWevDmIBLMZjOSkpJ4swohzFsW85bF+i2Pc1wW85bFmiKL81seM5fFvGWxhsvi/JbFvF2b6IK5t7c3vv/+e04Wumc6RYc6PnWgU/irLRLMZjNSU1P5syuEecti3rJYv+Vxjsti3rJYU2Rxfstj5rKYtyzWcFmc37KYt2sTr0qNGjXC4cOHpd+WiIiIiIiIiIiIiKhQ4gvmb731FmbPno0tW7ZIvzURERERERERERERUYEM0m/4/PPP48aNG+jQoQPuu+8+VKlSBYqiaI8rioIDBw5Id4tcjEk1Yd+NfTCpJkd3pVzQ6XSoWrUq7+4shHnLYt6yWL/lcY7LYt6yWFNkcX7LY+aymLcs1nBZnN+ymLdrE18wDwkJQcWKFaXfltyMGWbsvb7X0d0oNyyFnmQwb1nMWxbrtzzOcVnMWxZriizOb3nMXBbzlsUaLovzWxbzdm3iX3Ns3rwZmzZtKvQPUVEMigFxwXEwKOLf+ZRLJpMJR48ehcnEb/4lMG9ZzFsW67c8znFZzFsWa4oszm95zFwW85bFGi6L81sW83Zt/L0AckkKFER4RUCBUvTGdM9UVUVGRgZUVXV0V8oF5i2Lecti/ZbHOS6LectiTZHF+S2Pmcti3rJYw2Vxfsti3q7NIV/jXb16Ff/+97+xYcMGpKWloWLFiujYsSNGjx6N++67zxFdIiIiIiIiIiIiIqJyTvwM8+TkZDRp0gRvvPEGMjIyUL16daSnp2P69Olo0qQJLl68KN0lIiIiIiIiIiIiIiL5BfMJEyYgOzsbu3btwpEjR/Dzzz/jyJEj2LVrF7KzszFhwgTpLpELMqkmbM3YyrtpC9HpdIiKiuLdnYUwb1nMWxbrtzzOcVnMWxZriizOb3nMXBbzlsUaLovzWxbzdm3iey0xMREzZsxA8+bNrdqbN2+O119/HWvXrpXuErkgM8w4cfMEzDA7uivlgk6nQ1hYGAu9EOYti3nLYv2Wxzkui3nLYk2Rxfktj5nLYt6yWMNlcX7LYt6uTXyvZWRkoEaNGnYfq1mzJjIyMmQ7RC7JoBjQJ7QP76YtxGQy4cCBA7y7sxDmLYt5y2L9lsc5Lot5y2JNkcX5LY+Zy2LesljDZXF+y2Lerk18wbxmzZpYs2aN3cfWrl2LmjVrCveIXJECBUGGIN5NW4iqqsjOzubdnYUwb1nMWxbrtzzOcVnMWxZriizOb3nMXBbzlsUaLovzWxbzdm3iX+M99dRTePXVV2E2mzF48GBUqVIFly5dwhdffIH3338fb731lnSXiIiIiIiIiIiIiIjkF8zHjRuHpKQkzJ8/Hx988IHWrqoqnn32WYwdO1a6S0RERERERERERERE8gvmiqLgv//9L1566SVs2rQJaWlpCAkJQYcOHVCnTh3p7pCLMqpGJF5NhFE1Oror5YJer0dMTAz0er2ju1IuMG9ZzFsW67c8znFZzFsWa4oszm95zFwW85bFGi6L81sW83ZtDruzwv3334/777/fUW9PLk6FiuScZEd3o9xQFAVBQUGO7ka5wbxlMW9ZrN/yOMdlMW9ZrCmyOL/lMXNZzFsWa7gszm9ZzNu1id/0M7+rV6/i1VdfRbdu3fCvf/0LR44ccWR3yIV4KB4YVHkQPBQPR3elXDAajdi9ezeMRn7zL4F5y2Lesli/5XGOy2LeslhTZHF+y2Pmspi3LNZwWZzfspi3axM5w3zs2LH45ptvcO7cOa0tKysLzZo1w9mzZ7U7xn711Vf47bffeOY5FQv/UZVlMpkc3YVyhXnLYt6yWL/lcY7LYt6yWFNkcX7LY+aymLcs1nBZnN+ymLfrEjnDfPv27XjiiSes2ubPn48zZ85g9OjRSE9Px/bt2+Hn54e33npLoktERERERERERERERFZEFsxPnTqFZs2aWbV9//33CA0NxezZsxEQEIBWrVrhpZdewubNmyW6RERERERERERERERkRWTBPD09HVWqVNH+brmOT2xsrNXdYhs3boxLly5JdIlcnFE1YkXqCt5NW4her0fDhg15d2chzFsW85bF+i2Pc1wW85bFmiKL81seM5fFvGWxhsvi/JbFvF2byIJ5pUqVrBbC9+7di7y8PJuzznU6Hby8vCS6RC5OhYosUxZUqI7uSrnh6enp6C6UK8xbFvOWw/rtGJzjspi3HNYUeZzf8pi5LOYthzVcHue3LObtukQWzJs2bYqPP/5Yu7nnkiVLoCgKHnnkEavtjh07ZnUmOlFBeDdtWSaTCXv27OENK4Qwb1nMWxbrtzzOcVnMWxZriizOb3nMXBbzlsUaLovzWxbzdm0GiTd55ZVX0KZNG9x///2oWLEidu7cibZt26JJkyZW233//fdo3ry5RJeIiIiIiIiIiIiIiKyInGHesmVLfPvttwgPD8f169cxbNgwrFq1ymqby5cv48KFC+jRo4dEl4iIiIiIiIiIiIiIrIicYQ4AXbt2RdeuXQt8vHLlyjhw4IBUd4iIiIiIiIiIiIiIrCiq5cLiVKDMzEwEBgYiIyMDAQEBju6OQ0S/E+3oLtjwUDyQp+Y5uhuapLFJju5CmVFVFSaTCXq9HoqiOLo7bo95yyoPeTtbDXe2+g2whlPpcfe8na2eAKwpktx9fjsjZi7L3fNmDS8e1nAqDczb+ZRkfVfkkixEpU2BAl+9LxSw6EjJzc11dBfKFeYti3nLYf12DM5xWcxbDmuKPM5vecxcFvOWwxouj/NbFvN2XVwwJ5dkUAzoE9oHBkXsqkLlmslkwsGDB3l3ZyHMWxbzlsX6LY9zXBbzlsWaIovzWx4zl8W8ZbGGy+L8lsW8XRsXzImIiIiIiIiIiIiIUA4WzOPj46EoitWfypUrO7pbRERERERERERERORkysXvvTzwwANYv3699ne9Xu/A3lBpcbYbg7g7/tzIYt6ymLcs1m95nOOymLcs1hRZnN/ymLks5i2LNVwW57cs5u26FFVVVUd3oizFx8dj9erV2L9//12/RknuouqunPFu2s7GXe+kTUSujzW8aKzhRMXDelI8rClE5IxYw4uHNZzIPZVkfbdcnGF+8uRJhIeHw8vLCy1btsSbb76JqKioArfPyclBTk6O9vfMzEwAgNFohNFoBADodDrodDqYzWaYzWZtW0u7yWRC/u8iCmrX6/VQFEV73fztAGxuDlBQu8FggKqqVu2KokCv19v0saD2wsYE3L4hR/67V5tUE8ww27QbVSNUqPBQPKz6WFB7npoHBYrNjT7statQYVSN0EOPqhWq4lLOJaj//z+jaoQOOuiVv77BM6tmmGCCHnrolL+uQFRQ3+9lTPn3oaP2U1nNPVVVcePGDQQGBlq9pyuPqbB2R4/JbDYjMzMTAQEBMBgMbjEmZ95PiqLg2rVr8Pf3h6IobjGmO/eTh+JRJnUvv+LWcgUKKntWxvmc81CgWNVsh9byO3IH3OfnSVVVq5riDmMqTrujxmSvprj6mPK3W35+JI737qwF9toVKKjkWQkXci5AB12ZH+8Vd0yO3k8WpT33jEajVk90Op1bjMnZ95Oqqrh27RoCAgKsaoorj8le351lTJbPPUFBQTCbzW4xpvztjvjsXlgtNygGVPGqgks5l2BSTWKf3Yvqu9FodMufJ71ej/T0dPj5+Wn1xNXH5Mz7yXIMft9990FRFLcYU1F9d/Yx3TmOwrj9gnnLli2xePFi1KlTB//73/8wY8YMtG7dGkeOHEFISIjd58ycORPTpk2zad+3bx98fX0BAKGhoYiOjsbp06eRmpqqbVO1alVUrVoVJ06cQEZGhtYeFRWFsLAwHD58GNnZ2Vp7TEwMgoKCsG/fPquJ07BhQ3h6emLPnj1WfWjWrBlyc3Nx8OBBrU2v16N58+bIyMjAsWPHtHZvb280atQIV65cwalTp7T2wMBA1K1bFxcvXsSFCxe09sLGBAAd7+uICK8IrX1rxlacuHkCPSr2QJAhSGtPvJqI5JxkDKg0wOofoxWpK5BlysKgyoOsxrT48mL46n3RJ7SP1pan5mHx5cUI9wpHXHCc1p5uTMeK1BW43+d+9K/UH+dzzkNVVSTnJCPxaiIe9H8Qjf0aa9ufuHkCWzO2onVga9TxqaO177uxD3uv7y3VMeXfV47aT2U19yzFpX79+jhy5IhbjAlw3v108+ZNpKenIygoCHXr1nWLMTnzfoqMjMT+/fvh7e2tHTi6+pju3E+DKg8qk7qXX3FruaLcXjCfdXYWanrXRNvAttr2jqzlJpPJ4fsJKJu5p6qqVlOaN2/uFmNy5v0UHByMPXv2WC2Yu/qY8u8ny8++xPFeLZ9aRdYIRVFwn+E+vHv+XbQKaFXmx3vFHZOj95NFac+9o0ePavXEx8fHLcbk7PtJr9fjt99+Q1BQkFZTXH1MzryfVFVFbm4u2rRpgyNHjrjFmIC/9pMjPrsXVsub+DdBNa9qOJ9zHsezjot9di9qTPv27XPLn6fGjRvj8OHD2klZ7jAmZ95PlmPwv/3tb/D29naLMbn6fsrKykJxuf0lWe6UlZWF6OhojB8/Hi+99JLdbeydYV6tWjWkpaVpp+yXt29ras+t7fCzEoG/vo32UrzwZOUn8eX/vrx9Fo8TnGF+eNRhrd3Zv1Wz9LG4c89kMmHv3r1o1qyZ9g+rq4+psHZHj8loNGLv3r1o0qQJPD093WJMzryfzGYzdu/ejSZNmmjv5epjunM/1Z9X32nOMPdQPDCw0kAkXE6ASTU5zRnmJ18+6fD9lL+9NOeepYZbaoo7jKk47Y4ak72a4upjyt9ef159AI4/K9HSnr+mmFWzw89KtLT/+fKfbvnzlJubq9UTg8HgFmNy9v1kNBqxZ88em5riymOy13dnGZPl38zmzZtDURS3GFP+9vvn3O9UZ5hX0FXAwEoD8eX/vkSOOcdpzjA/POqwW/48AbA5RnH1MTnzfsq/jmIwGNxiTEX13dnHlJmZiZCQEF6SxR5fX180aNAAJ0+eLHAbLy8veHl52bQbDAbtV5ktLDvgTpZJUtz2O1/3btoVRbHbXlAfS9puVO3/6kJB7QXdvMNeuwq1RO1mmLXH8j9uhhlm1WyzvQkmmFTbfyxKc0z2snfEfiqruacoSoF9d9UxFdbuyDGpqqr9I2H5gsLVx1TcPpa0vTTGZDabtbzvfC1XHdOd7flrlzPUchW3D1gKqtmOqOUF5Q64x89TcWqKq42pOO2OGFNhNcVVx5S//c6fn7I+3itOjbDUFInjveL23dH76U6lNfcsdUSv19t8yVzcvjvbmJx9P1mOwYtbU1xhTCVtlx7TnZe+uZe+F9TuqP3kqM/uBdVyy8lveWoeTLhdv52hluc/fnKnnyej0VhgPXHVMQHOvZ8sNdzdP2sUp90ZxlRQf+2xfQc3l5OTg6NHj6JKlSqO7grdAxUq0o3p2gckKluKolhdroLKFvOWxbxlsX7L4xyXxbxlsabI4vyWx8xlMW9ZrOGyOL9lMW/X5vaXZBk7diy6d++O6tWrIyUlBTNmzMCWLVtw6NAhREZGFus1SnIXVXfFu2kXjXfSJiJnxRpeNNZwouJhPSke1hQickas4cXDGk7knkqyvuv2Z5hfuHABAwYMwP3334/evXvD09MTO3fuLPZiOTknHXSo41MHOvefwk7BbDYjJSXF6jpRVHaYtyzmLYv1Wx7nuCzmLYs1RRbntzxmLot5y2INl8X5LYt5uza3r0pfffUVLl68iNzcXCQnJ2PFihWoV6+eo7tF90iv6NE2sK3VzUOo7JjNZpw6dYqFXgjzlsW8ZbF+y+Mcl8W8ZbGmyOL8lsfMZTFvWazhsji/ZTFv1+b2C+ZERERERMW1f/9+dO3aFdWrV4e3tzeCg4Px0EMP4YsvvijyuRcuXMBLL72E4cOHo2LFilAUBYsWLbK7bWxsrNWNoCx/4uLiSnlERERERERUEsW/PSgRERERkZtLT09HtWrVMGDAAERERCArKwtLlizBk08+iTNnzmDSpEkFPvfPP//E0qVLUbNmTXTp0gVfffVVoe8VFRWFJUuWWLUFBQWVxjCIiIiIiOguccGcXJIKFck5ybybthBFURAYGMi7Owth3rKYtyzWb3mc4yUTGxuL2NhYq7Zu3brh9OnTWLBgQaEL5g8//DAuX76MEydOIDMzs8gFc29vb7Rq1ao0ul1usabIYj2Rx8xlMW9ZrOGyOL9lMW/XxkuykEsyqkYkXk2EUTU6uivlgl6vR926daHX89pyEph3yWzcuBFPP/00YmJi4Ovri4iICPTo0QO///57sZ6/fv16DBs2DH5+fggMDET37t1x5MiRArd96KGH4OPjg4oVK2LIkCFISUkpzeG4PdZveawppaNixYowGAo/10Sn0zFvYawpsji/5TFzWcxbFmu4LM5vWczbtXHBnFySDjo08W/Cu2kLMZvNuHDhAm9WIYR5l8xHH32EM2fOYNSoUfjxxx8xb948pKSkoFWrVti4cWOhz/3222/RpUsX+Pv7Y9myZfjPf/6DkydPom3btkhKSrLadsuWLejSpQsqVaqEb7/9FvPmzcP69evxyCOPICcnpyyH6FZYv+Wxptwds9kMo9GI1NRUfPjhh1i3bh1eeeWVYj2vuHknJSUhODgYBoMB0dHRmDhxIrKzs0uj++UGa4os1hN5zFwW85bFGi6L81sW83ZtvCQLuSS9okdjv8Y4dOMQzCqLT1mzFPrKlStDp+PBTFlj3iXzwQcfICwszKotLi4OtWrVwptvvokOHToU+NxXXnkF9evXx9SpU9G8eXMYDAa0bt0aderUwZQpU6yuLTxu3DjUqVMHy5cv184yrVmzJtq0aYNPP/0Uw4cPL5sBuhnWb3msKXfn+eefx3//+18AgKenJ9577z3861//KvJ5lryL8re//Q39+/dHTEwMsrOzsXbtWsyePRu//vorNm3axH1VTKwpslhP5DFzWcxbFmu4LM5vWczbtXHBnIiIXNqdi+UA4Ofnh3r16uH8+fMFPi8tLQ3Hjx/HuHHjrK4rFxkZifr162P16tUwmUzQ6/VITk7G7t27MXPmTKtLMlgW11etWsUFcyI3M2HCBAwbNgwpKSn4/vvvMXLkSGRlZWHs2LGl8vozZsyw+vtjjz2GGjVqYOzYsfj222/Rq1evUnkfIiIiIiIqGX7FQUREbicjIwN79+7FAw88UOA2ubm5AAAvLy+bx7y8vHDz5k3tsiyHDx8GADRs2NBm24YNG2qPE5H7qF69Opo1a4bHHnsMH330EZ599lm89tprSE1NLbP3/Oc//wkA2LlzZ5m9BxERERERFY4L5uSSzKoZJ26e4K9tCdHpdAgNDeWvEQlh3vduxIgRyMrKwsSJEwvcplKlSggODsaOHTus8k5PT9cWwNPS0qz+Pzg42OZ1goODtcepaKzf8lhTSkeLFi1gNBpx6tSpQrcrjby5r4qPNUUW64k8Zi6LectiDZfF+S2Lebs27jVySSaYsDVjK0wwObor5YJOp0N0dDQLvRDmfW8mT56MJUuW4N///jeaNm1a4HY6nQ4jRozAhg0b8OWXX+LKlSv4888/8c9//hM3b97Utskv/6VbitNOtli/5bGmlA7LdcWjoqIK3e5e8k5ISAAAtGrV6q76WB6xpshiPZHHzItv48aNePrppxETEwNfX19ERESgR48e+P3334t87vr16/Hoo4+iatWqqFevHipXrowOHTrgxx9/tNk2NjYWiqLY/ImLiyuLYbk11nBZrCeymLdr4zXMySXpoUfrwNbYnrGd/7gKMJvNOH36NGrWrMliL4B5371p06ZhxowZeOONNzBy5Mgit58yZQquX7+O6dOnY8qUKQCArl274qmnnsInn3yCiIgIAEBISAgA2D2T/OrVq3bPPCf7WL/lsaaUzLPPPouAgAC0aNEClSpVwpUrV7Bs2TJ8/fXXGDduHEJDQwEAQ4cORUJCApKSkhAZGak9/5tvvkFKSgqysrIAAHv27IGfnx8AoG/fvgCArVu34o033kCvXr0QFRWFW7duYe3atViwYAE6dOiA7t27C4/adbGmyGI9kcfMi++jjz5CWloaRo0ahXr16iE1NRVz5sxBq1atsG7dukJvBJ+WloYHHngATz/9NFRVhZeXFxYsWICuXbvi888/1y6ZZREVFWV1c3gACAoKKothuTXWcFmsJ7KYt2vjgjm5JJ2iQx2fOtiZuRMmlf+wljWz2YzU1FRERkay0Atg3ndn2rRpiI+PR3x8PCZMmFCs5xgMBrz99tvo3r077rvvPlSuXBlVqlRB586dUbNmTVStWhUAUL9+fQDAoUOH8Nhjj1m9xqFDh7THqWis3/JYU0rmoYcewmeffYaEhASkp6fDz88PjRo1slkwMZlMMJlMUFXV6vn9+/e3+vsHH3yADz74AAC0batUqQK9Xo/p06fjypUrUBQFtWvXxuuvv46XX36Z+6kEWFNksZ7IY+bF98EHH9jcDD4uLg61atXCm2++WeiCef/+/dG/f38YjUbs2bMHzZo1w9///nfUrFkTCxYssFkw9/b25m8DlQLWcFmsJ7KYt2vjgjkREbm86dOnIz4+HpMmTcLUqVNL/HwfHx80aNAABoMBe/fuxYYNGzBnzhzt8YiICLRo0QJffPEFxo4dC71eD+D2jfmOHz+O0aNHl9ZQiMjBnnrqKTz11FNFbrdo0SIsWrTIpj0vL09bbDEY7B9q16pVC2vWrLnXrhIRUT53LpYDgJ+fH+rVq4fz58+X+PU8PDwQFBRUYC0nIiL3xcpPREQubc6cOZgyZQri4uLQtWtX7Ny50+pxy9k/9i6fsHnzZuzatQteXl5IS0vD77//jlmzZiEuLs7mki6zZs3Co48+in79+uH5559HSkoKXn31VdSvX79Yi2tEREREJCsjIwN79+4t9Ozy/MxmM4xGIy5evIiFCxfixIkTmDVrls12SUlJCA4ORmZmJiIjI/HEE09g0qRJ8Pb2Lu0hEBGRA3DBnFySSTVh3419/LUtITqdDlWrVuWvEQlh3iXz/fffAwASExORmJho87jlEgj2Lp/g6emJVatW4Y8//kBubq52SYQXX3xRO4vcIjY2Fj/++COmTJmC7t27w8fHB926dcPbb78NLy+vMhyhe2H9lseaIot5y2JNkcX5LY+Z35sRI0YgKysLEydOLNb23bt3x08//QQACAgIwNdff42uXbtabfO3v/0N/fv3R0xMDLKzs7F27VrMnj0bv/76q3aDaCoe1nBZrCeymLdrU9Q7L7xINjIzMxEYGIiMjAwEBAQ4ujsOEf1OtKO74PSSxiY5ugtERHaxhheNNdx1cX4XT2nNceZdPKwpRI43efJkzJgxA++//36xbgYPACdPnkR6ejouXbqEL774AqtXr0ZCQgIGDBhQ6PPmzJmDsWPHYuXKlejVq1dpdL9MsIYXD2s4kXsqyfouv+Ygl2RQDIgLjoNB4S9JSDCZTDh69ChMJn7zL4F5y2Lesli/S+769esYP348OnXqhNDQUCiKgvj4+GI9NzY2FoqiFPjn8uXLAG4fPL7xxhuIjY1F5cqV4efnhwYNGmDWrFm4detWGY7O/XCOy2LesvhvpjxmfnemTZuGGTNm4I033ij2YjkAREVFwc/PD127dsU333yDRx55BCNGjIDZbC70eZabgt55aUAqHGu4LNYTWczbtXHBnFySAgURXhFQoDi6K+WCqqrIyMgAfyFFBvOWxbxlsX6XXFpaGhYsWICcnBz07NmzRM/98MMPsXXrVnz88cfYunUrduzYgQ0bNsDDwwOtWrVC5cqVAQDnzp3Du+++iyZNmmDBggX47rvv0LdvX8THx6Nbt278+SgBznFZzFsW/82Ux8xLbtq0aYiPj0d8fDwmTJhQoufemXeLFi1w7do1pKamFuv5vPRCybCGy2I9kcW8XRu/xiMiIlHO9qugHooHBlUehCHbhiBPzXN0dzT8VVCyiIyMxLVr16AoCq5cuYJPPvmk2M+tV68ejEYjDAYDmjVrBoPBgISEBOTl5WHYsGHadjVr1sSZM2fg6+urtXXo0AG+vr4YN24ctm3bhr/97W+lOi4iIqLSNn36dMTHx2PSpEmYOnXqPb2WqqrYsmULgoKCEBISUui2CQkJAP662TwREbk2LpgTEREROTFFKd2zrhYuXAg/Pz/0799fa8u/UJ5fixYtAADnz58v1T4QERGVtjlz5mDKlCmIi4tD165dbS6PYlnMHjp0KBISEpCUlITIyEgAQI8ePdCoUSM0aNAAqamp+PPPP/H5559jy5Yt+OCDD2Aw3F462bp1K9544w306tULUVFRuHXrFtauXYsFCxagQ4cO6N69u+ygiYioTHDBnFySSTVha8ZW3k1biE6nQ1RUFH/FUAjzlsV6Iot5y8tfU06ePImtW7di2LBh8PPzK/K5GzduBAA88MADZd1Nt8E5Lot5y+IxijxmXnzff/89ACAxMRGJiYk2j1sui2AymWAymawuk9CmTRssX74c8+fPR2ZmJoKCgtCsWTP88MMP6Nq1q7ZdlSpVoNfrMX36dFy5cgWKoqB27dp4/fXX8fLLL3M/lRBruCzWE1nM27VxwZxckhlmnLh5wtHdKDd0Oh3CwsIc3Y1yg3nLYj2Rxbzl5a8pCxcuBHD77LqiHDx4ELNnz0avXr3QsGHDMu2jO+Ecl8W8ZfEYRR4zL77NmzcXa7tFixZh0aJFVm3jx4/H+PHji3xurVq1sGbNmrvoHdnDGi6L9UQW83Zt/JqDXJJBMaBPaB/eTVuIyWTCgQMHeHdnIcxbFuuJLOYtz1JTcnJykJCQgAceeKDIa6yeOXMG3bp1Q7Vq1Up0zXTiHJfGvGXxGEUeM5fFvGWxhsvi/JbFvF0bF8zJJSlQEGQI4t20haiqiuzsbN7duZiuX7+O8ePHo1OnTggNDYWiKIiPjy/Wcy9cuIAxY8ZgyJAhCAkJgaIoNmfAALcXsxRFKfBPXFxc6Q7KjbGeyGLe8iw1/Mcff8Tly5etbvZpz9mzZ9G+fXsYDAZs2LABwcHBQj11D5zjspi3LB4TymPmspi3LNZwWZzfspi3a+PXeEREpSwtLQ0LFixAo0aN0LNnzxKdnfnnn39i6dKlqFmzJrp06YKvvvrK7nZVqlTBjh07bNpXr16NWbNmoVevXnfdfyJyT5999hk8PT3x5JNPFrjN2bNnERsbC1VVsXnzZlStWlWwh0REVN5EvxPt6C5Y8VA8MKjyIAzZNgR5ap6ju6NJGpvk6C4QEZUrPMOciKiURUZG4tq1a9iyZQtmzpxZouc+/PDDuHTpEt577z2MGjWqwO28vLzQqlUrmz+7du2Cj48PBgwYcK/DICI3kpaWhrVr16Jnz54ICQmxu825c+cQGxsLk8mEjRs3IjIyUriXRCThxo0bGD16NMLDw1GhQgU8+OCDBX5Bf6effvoJzz77LPz9/REYGIju3bvjyJEjVttkZmbijTfeQGxsLCpXrgw/Pz80aNAAs2bNwq1bt8piSERERESligvm5JKMqhGJVxNhVI2O7opLuJcPRps2bUKXLl3QvXt3BAUFoWHDhnjvvfesrsPFy4NYs4z7buh0Ouj1esTExECv15fouUlJSdiyZQsef/xxBAQE3NX7l0esJ7KY991Zu3Ytli9fju+//x4A8Mcff2D58uVYvnw5bt68CeD2jTwNBgPOnj1r9Vy9Xo/du3fDaDQWeDmWlJQUtG/fHpcuXcLMmTORkpKCnTt3an8uXLhQtgN0I5zjsph3yfXu3RsJCQmYOnUq1q5di+bNm2PAgAH48ssvC33et99+i27duqF69epYvnw5/vOf/+DkyZNo27YtkpL+Ovv13LlzePfdd9GkSRMsWLAA3333Hfr27Yv4+Hh069aNv5peQnd7XEh3hzVFFvOWxXoii3m7Nl6ShVySChXJOcmO7obL6N27N3bv3o233noLderUwZdffokBAwbAbDZj4MCBBT5v/fr16Ny5Mx5++GF88skn8PX1xXfffYdRo0YhKSkJ8+bNA8DLg5Q2RVEQFBRU4kX3Tz/9FKqqFnl9YrLGeiKLed+d4cOHWy2EL1u2DMuWLQMAnD59GjVq1IDJZILJZLJZjFIUBUuXLkWNGjXQsWNHu6//xx9/4NSpUwCAf/7znzaPT506tdj3YijvOMdlMe+S+fHHH/Hzzz9rx4IA0L59e5w9exbjxo1D//79C/xg/8orr6BBgwb4/vvvtWOU1q1bo06dOpgyZQqWLFkCAKhZsybOnDkDX19f7bkdOnSAr68vxo0bh23btuFvf/tbGY/UfViOC0kGa4os5i2L9UQW83ZtPMOcXJLl2nIeioeju+L0LB+MPvzwQ/zrX/9C+/bt8fHHH+PRRx/FuHHjCr1j86JFi+Dh4YHVq1cjPDwcsbGxeO+999CpUyerG1Hy8iCly2g0ameDFpfJZEJCQgJiYmLQpk2bMuyd+2E9kcW8786ZM2egqqrdPzVq1ABwu2bn/7uF0WjEF198gZMnTxb4RZzluuUF/eFiefFxjsti3iWzatUq+Pn5oV+/flbtTz31FC5evIhdu3bZfV5aWhqOHz+Ozp07Y8+ePdoxSmRkJOrXr4/Vq1drx5S+vr5Wi+UWLVq0AACcP3++NIfk9u7muJDuHmuKLOYti/VEFvN2bVwwJ5fFf1SL524/GAGAh4cHPD094e3tbbWwHhQUhAoVKhT6vrw8yL0p7IsMexITE5GcnIyhQ4eWUY/cG+uJLOYtr6Q1he4N57gs5l18hw8fRt26dWEwWP+iccOGDbXH7cnNzQVw+ySJO+uJl5cXbt68aXVZFns2btwIAHjggQfuqu/lGWu4LNYUWcxbFuuJLObturhgTuTm7vaDEQA899xzyM3NxejRo5Gamor09HR8/vnnWLVqFcaPH1/o+/LyILIWLlwIDw8PDBo0yNFdISIiIieVlpaG4OBgm3ZLW1pamt3nVapUCcHBwdi+fbtVe3p6unYsWdBzAeDgwYOYPXs2evXqpR2DEhERETkrXsOcyM2lpaUhKirKpr2oD0YA0LJlS2zcuBH9+vXDRx99BOD2jStmzpyJl19+ucDn8fIgslJSUvDDDz/g73//O8LCwhzdHaJyL/qdaEd3wYrl152HbBuCPDXP0d3RJI0t/GxUIiobhd0jpaDHdDodRowYgenTp+PTTz9F9erVcfPmTYwePVq78bBOZ/9crDNnzqBbt26oVq0aPvnkk3sfABEREVEZ4xnm5JKMqhErUlfwbtrFdDcfjADg999/R69evdC0aVMsX74cGzZswGuvvYZJkyZh+vTpBT6Plwe5N3q9Hg0bNiz23bQXL16MvLw85n2XWE9kMW95zFwW85bFvEsmJCTE7skSV69eBQC7Z59bTJkyBaNHj0ZCQgIiIiJQu3ZtALcv8wcAERERNs85e/Ys2rdvD4PBgA0bNhT6+mRfSY8L6d6wpshi3rJYT2Qxb9fGM8zJJalQkWXKggrV0V1xevfywWjEiBGoVKkSVq5cCeB2we/QoQN0Oh3i4+Pxj3/8w+7Z67w8CLB27VpkZWXh+vXrAIA//vgDy5cvBwA89thj8PHxwdChQ5GQkICkpCRERkZqz12+fDnMZjPOnDkDANizZw/8/PwAAH379rV5r4ULF6JatWro3LlzGY/KPbGeyGLe8pi5LOYti3mXTIMGDbB06VIYjUary/UdOnQIAFC/fv0Cn2swGDB37lxMnToV586dQ2hoKKpUqYLOnTujZs2aqFq1qtX2Z8+e1W4ovHnzZpvHqfg8PT0d3YVygzVFFvOWx3oii3m7Lp5hTi6Jd9MuvgYNGuDo0aM2d2Yuzgej/fv3o2nTpgBuL9pabljRvHlzmM1mHD161OY5vDzIbcOHD0e/fv3w9NNPAwCWLVuGfv36oV+/fkhJSQFw+9I1JpMJqmp9gPj444/jiSeewKuvvgoA+OCDD7Tn3mn79u04duwYnnrqqQJ/FZoKx3oii3nLY+aymLcs5l0yvXr1wo0bN7BixQqr9oSEBISHh6Nly5aFPt9kMuHYsWOoV68eqlSpgr1792LDhg0YNWqU1Xbnzp1DbGwsTCYTNm7caHViAJWMyWSyOg6nssWaIot5y2I9kcW8XRvPMCdyc7169cLHH3+MFStWoH///lp7cT4YhYeH2y3wO3bsAAC7Zwrx8iC3Wc4OL8yiRYuwaNEim/a8vDzs2bMHzZo1s7lZ651at25ts+BOREREZE+XLl3w6KOPYvjw4cjMzEStWrWwdOlSJCYm4osvvtB+bdzeb8Ft3rwZu3btgpeXF9LS0vD7779j1qxZiIuLw8iRI7X3SElJQfv27XHp0iUsXLgQKSkp2skCwO3jR55tTkRERM6MpyMSubn8H4w+/vhjbNq0Cc8++ywSExMxe/Zsqw9GBoMBZ8+e1Z47ZswYHD58GD179sQvv/yC9evX49VXX8Xs2bPRsWNHNGrUyOb9eHkQIiIiIue1cuVKPPnkk5gyZQri4uKwa9cuLF26FP/4xz+0bez9FpynpydWrlyJSZMmoU+fPli2bBlef/11rFq1yur6rH/88QdOnTqFnJwc/POf/8RDDz1k9Yc3/iQiKh15eXmYNm0aatSoAS8vL8TExOD9998v9vNv3LiB0aNHIzw8HBUqVMCDDz6Ir776yu62e/fuRceOHeHn54egoCD07t0bp06dKq2hEDkdnmFOVA6sXLkSEydOxJQpU3D16lXExMRg6dKleOKJJ7Rt7H0weuGFFxAREYG5c+di5syZyMvLQ40aNTB16lSMGTPG5n0slweZMmUKLw9CRERE5IT8/Pwwb948zJs3r8Bt7P0WXOvWrbFt27YifwvOct1yIiIqW88//zw+//xzTJ8+Hc2bN8e6deswatQoXL9+HRMmTCjy+b1798bu3bvx1ltvoU6dOvjyyy8xYMAAmM1mDBw4UNvu2LFjiI2NxYMPPohvvvkGt27dwpQpU9C2bVvs378foaGhZTlMIodQVB7NFCkzMxOBgYHIyMhAQECAo7vjENHvRDu6CzY8FA/kqXmO7oYmaWySo7tQZlRVhclkgl6vh6Ioju5OqeP8Lp7SmuPMu3hKs6Y4W+bMW547Z868i4d5y3LX40J3PyZ0Ru6eOWtK8bCGy3KFGn7kyBE0aNAAb7zxBl577TWt/dlnn8UXX3yBCxcuIDg42Oo5+evJ2rVr0bVrV22R3KJTp044cuQIzp07p/320OOPP45NmzYhKSlJWxM7e/YsateujTFjxmDWrFkCI3Y97l6/XVFJ1nd5Cii5JAUKfPW+UMCiIyU3N9fRXSg3OL9lMW9ZzFseM5fFvGUxb3k8JpTHzOWwpshi3ndv9erVUFUVTz31lFX7U089hezsbCQmJtp9nqWerFq1Cn5+fujXr5/N8y9evIhdu3YBAIxGI3744Qf06dPHaoExMjIS7du3x6pVq0pzWE7tbi6BY8l748aNePrppxETEwNfX19ERESgR48e+P33322eo6oq3nvvPcTExMDLywtVqlTB8OHDce3atTIZF9nHS7KQSzIoBvQJ7YPFlxc73bfR7shkMuHgwYPFugkl3TvOb1nMWxbzlsfMZTFvWe6et7OdDeqheGBQ5UFOl7crnA0K3F5sefPNN/HZZ5/h0qVLqFmzJkaMGIEXXnihwOdYjsMNBgOmTp2KQ4cOITU1Fd7e3rj//vsxYsQI/POf/7R53t69ezF+/Hjs3LkTBoMBHTp0wDvvvIOoqKiyHKLLc/ea4myY9907fPgwQkNDUblyZav2hg0bao/fKf/n+sOHD6Nu3bo2n+/zP79169ZISkpCdna21n7ntj///DNu3bqFChUqlNbQnFZJL4GTP++PPvoIaWlpGDVqFOrVq4fU1FTMmTMHrVq1wrp169ChQwfteWPHjsW7776LsWPHomPHjvjjjz8wZcoU7N69Gzt27ICHh4fksMstrnwROSFn/XA0ZNsQpzqQcZUPR0RERERE93K94YyMDFSrVg0DBgxAREQEsrKysGTJEjz55JM4c+YMJk2apG3L6w0Tub+0tDSbS64AgK+vLzw9PZGWllbk8+19gWZ5TcvzLf9v772Cg4OhqiquXbuGKlWqlHgMruTIkSNYuHAh3njjDYwbNw7A7Xt2pKWlYcaMGXjuuefsZmTxwQcfICwszKotLi4OtWrVwptvvqktmCcnJ2PevHkYMWKEdqmbRx99FGFhYRg4cCAWLVqEZ555poxGSflxwZyIiIiIiIioDN3rYku7du3wyCOPWLV169YNp0+fxoIFC6wWzKdMmQIvLy/88MMP2iUUmjZtitq1a+Odd97h9YaJXIzRaLT6u+Xa4oVdF7s418wuyfPv9b1cXWGXwPn444+RmJhodaPUO925WA7cvgl3vXr1cP78ea1t586dMJlMeOyxx6y27datGwBgxYoVXDAXwmuYk8typjOdywPmLYt5y2Lespi3PGYui3nLYt6ymPfdudvrDQN/LY7ZU7FiRatLKvB6w/eOc1wW8y7cmTNn4OHhYfVny5YtCAkJsXsWeVZWFnJzcwv8As5STwp6/tWrVwH8dUZ5SEgIABS4raIoCAoKuquxuZK7uQQOUHj9zsjIwN69e/HAAw9obZZrnnt5eVlt6+HhAUVRcPDgwbvqP5UczzAnl5Sn5mHx5cWO7ka5wbxlMW9ZzFsW85bHzGUxb1nMWxbzvnt3u9hiMBjQvHlz7e9msxlmsxnXrl3DsmXLsG7dOsyfP197nNcbvjec47KYd9HCw8Oxe/duq7b7778fDRo0wFdffYXLly9b1ZVDhw4BAOrXr2/zWvnrSYMGDbB06VIYjUarL93ufH50dDS8vb219vwOHTqEWrVqlYt6cjeXwLmzft9pxIgRyMrKwsSJE7W2evXqAQC2bduG9u3ba+3bt2+HqqpFXmqHSg/PMCeXpEBBhFcE76YthHnLYt6ymLcs5i2Pmcti3rKYtyzmfffu9nrDqqoiPT0dqqoCuH0ddA8PD4SFhWHMmDF477338K9//cvqfYCirzdM9nGOy2LeRfP09ESzZs2s/vj7+6NHjx5QFAUJCQlW2y9atAje3t6Ii4uzea389aRXr164ceMGVqxYYbVNQkICwsPD0bJlSwC3F327d++OlStX4vr169p2586dw6ZNm9C7d+8yGLVjGY1Gqz+W+lvSy9LcWb/zmzx5MpYsWYJ///vfaNq0qdbeqFEjPPzww3j77bexbNkypKenY/v27Xjuueeg1+uh03EZVwqTJpdkUAyIC46DQeEvSUhg3rKYtyzmLYt5y2Pmspi3LOYti3kXT2kttgCAyWTCsWPHYDKZAAATJkzA7t27sWbNGjz99NMYOXIk3nnnnWK/XlGPlXec47KY99174IEHMHToUEydOhXvvPMOtmzZgokTJ2r3NMj/pdnrr78Og8GATZs2afWkS5cuePTRRzF8+HB8/PHH2LRpE5599lkkJiZi9uzZVpcSmTZtGm7evIlu3bph7dq1WLVqFbp27YqKFSvi5ZdfdsTwy0xpXgLnzvptMW3aNMyYMQNvvPEGRo4cafO8ZcuWoU2bNnj88cdx3333oX379ujduzcefPBBRERElN5gqVDlpip9+OGHePvtt3Hp0iU88MADePfdd9G2bVtHd4uIiIiIiIjcxJkzZ1CzZk2rtk2bNiEkJAT79++32b6o6w3fqXr16qhevToAaDeFe+211zB48GCEhobyesNE5ciHH36IiIgIvP/++7h8+TJq1KiBefPm4YUXXrDazmw2w2Qy2ZzpvHLlSkycOBFTpkzB1atXERMTg6VLl+KJJ56w2i4mJgabN2/GK6+8gr59+8JgMKBDhw545513EBoaWubjlFSal8CxZ9q0aYiPj0d8fDwmTJhgd5uwsDD8+OOPSElJweXLlxEZGQlvb298+OGH6Nu3712OjEqqXJxh/vXXX2P06NGYOHEi9u3bh7Zt26JLly44d+6co7tGREREREREbsKy2JL/T9OmTdGgQQOkpqbi8uXLVtuXdLHlTi1atIDRaMSpU6cA8HrDROWJh4cH4uPjcfbsWeTk5OD48eM2i+UAEB8fD1VV0a5dO6t2Pz8/zJs3D5cuXUJOTg4OHDhgs1hu0bRpU6xfvx5ZWVnIyMjAqlWrEB0dXSbjcqTSvATOnaZPn474+HhMmjQJU6dOLXL7sLAwNGzYEIGBgfjPf/6DrKwsu2ekU9koF2eYz507F0OHDsWwYcMAAO+++y7WrVuHjz76CDNnziz+C+Xm3v5zJ50OyHeTBLvbWCgK4OFxd9vm5QF2rn1UptsCgKen9p8GkwqlkE3zDH/9ep/epEJXWtvq/7/f/7+tAWZcv5UGg9Fs0/c7ty3u6+rMKvTme9g2/740GG7PCwAwmW7/KYidbT2M9jtt1AGqrnj9zb+tYlZhKGRbkw4wF7KtQfkrb5OiFrptSV63oG2hqvAoJLI7t0VeIXdWz//zWcC2lrzNCmDSF68PVtvme4173VZVANWgIt2YDhVqkdsa871uYT+fpbJtQfUqX40oST0pyxpRFvXknmtEPsX6WbbkfY/1BLA/50qzRpRk29KoJyWtEcX+WS6FegLczrssa4SxhNuquF1T9CYzYLa/fVnVE8D6Z9lqW3s1JX89MRoBcyGToiT15B5qREmPI/LP8fw1pdRrREm3zc29+3piNt/eH7A/50qzRpR0W0veitkM7RK4ZVgjyqSeAAX+G1vSenI3NcKiOD/3pV1PgEJqRBHbWv183ms98fDQfj7vdVtPAM0sN9zMt22Pbt0wadIkJHz6KV4ZO1bbftHChbcXWzp3/utF8v18KiYTfAwGKHl5f71Xvp/PTRs3QqfTIapqVSA3FwYA3bt2xcqVKzF7xgz433cfoNNp1xse8+KLBR/TlUKNsEuvv/3HzrZ3zlGJelLYz7JBMSMj9ypUqEVuC9xbjSjJZ41C1w1KsB5hMKllWiNKuq0KFZkFHIdblEqNKOm2d2ZYijWiwG2L+pkrybYF/CwXVU/u9nWL3PYeaoRDtrXzb/gDtWtj6FNPYerUqdArCpq3bImffvoJCxYswIxp0xDs56fNm9ffeAOvv/EGfvrxR4SEhEBRFMyZMwdTpkxBXKdO6NqpE3Zu3Wr1+q1attR+lj/++GNAVREdGYn0jAysXbcOCz/7DG9On44m9evffp+SrEOWozXLIrctAbdfMM/NzcXvv/+OV1991aq9U6dO2L59u93n5OTkICcnR/t7ZmYmAMD89tswe3kBuH3dN0VRoKoqzNHRUAcOBADodDro3n4b5nzPz7+9uXp1mAcN0tr1//43lOxsmO8oooqiAOHhMA0d+lfbe+9B9/99ufNXaXSVKkF9/nnt2kjKRx9BuXIFOp0Oqqpabx8UBN1LL2l3WFc++QTKpUtWY7Jsr/r4QBk/HsDt64v13peJiKu3C4cZZqhQoYMOChTk6RX8t2MIjKoRKlT8/eAt1Ej964fLsr0et4vQ/M4VAdy+M3bc4Zuo8z/r4mWCCQoU6KDDfx8JQZ7h9kF6+6MZqHcxFzok4Ln/31aFCjPMUKDgs9iKyPa6XZgfPnEdD5y/qfXxzr58/nAIbnjf/jFo82cWGp3JshpT/u2/aB2ATP/bZ2O0OH0TLZJuWo3JfHPGX/vq2WeBiIjb+2PbNug2bLi9n+zsD3XwYOijo2/vj127oEtMxMidmVZj0v3/L4P80CQApyrqYYIJ9S6Z8OiRLJsxWfqe2Mgff1b2hEk1ITrlFroevGk3Az30WF/fD8cibo+tatpNdN93XdtPf+2PT/EsFGyt649D1b0BABEZefj77qtWfcy/P7bX9sWBKD8AQNiNPPTbmW4zJsv2O6K9sCfaDzpFh+AbRgzcnm4zJovfI73wy/0Vbv9a2bVr0L333u3cLT9n+X6e1GbNoOve/fYYMjOhmzPnr/30/wccI3fc/rk6GuGFDfX9kafmwdOkYMTGDJsxWfr+ZyVPJD4YABUqjKoRz29Mt5uBDjqcDfXCD00CbvdBNeGZLWnwNCl298elYC+sbh6E7658BwUKntqagQp5Zpv9YYYZ/wvQY8VDIVrboO1X4ZudZ5MvAKT6AkvaBGnXBxy46xruu2G0uz8yvXVY2NYfOuigV/R4fE86wjKMULPfKLBG6HS62/N98WIoZ8/a3R+qhwfU116DAkWoRtjuD/s14hp0WITnyrhG5PdlS1+kBHrAoBjQ+NxNtDlx02ZMlppir0ZY3FlTzE88AV1MDHQ6HcwHDgCrV+P/2DvvqCqSJYzXEAUBEUFQQEWULFkRCSJmRAUD5jXnnEURc8RVVJRgzgrqqqtrzlnMipgzGJAoOdzv/XG9472AK0EuT7Z/57zzsGeGrakpvumu6a4mIhp1LUUKGiG0vWgasZkGS0Ej5Dg5UssUUN8LiQXuScQD/Up02rQSAfihRvDxbmVF6NiR5OTkCNnZhAULJHwgeh6jrqaUuUaI6H8xrkgacejLIfrjclLZaoTYPYlsz1DgKMxNnQQkKNCPEGlKfo2QlZUljuMob+dO4p4//+HzEPj788fKViNK0o8oqCm/WiNE7G+oRm+qEsmQDFnHZFPT6LQC9yTSlB9phMi/4s9D0Lkzkbm5sG8bHU2CPXuISKgn4j4oG40obj9iM9mUtUYQSC1HjgaeSyhwTyJbxPsR/6YRAAimpoSuXYnjOJKVlSUsWCDRPxQ9j5FXkqWiEURF60dwxNGhL4eo57XkMteI4ow1BOnzf6gRsrKyRPv2EaKifvg8BNOmESkoCJ/H4cOEO3cKjre+nZ83YQJR5crCtqNHSebWrQL9e9Hzyxs9mlClChERmcTE0ABra5rl708yZ8+Sfc2adPLFCwq7fJnmNWtGGnl5lPstmTN/0CCav3UrnerTh5rWqUMWAA0dMoTUFBWpYc2apNO9O33hOIqIiKDw8HCa5OhI1UJDib7ZOEtLi/5JSiJPW1uaPGsW5VStSv7+/qSppkbjBQISzJ9fwAdEP+5HiN/TzzSisPMFHToQWVsLn8eLF0Q7d/L/TZGmlK1GFLcfsYtyy1gjijvW+JFGFDcf4f04vUw1orj9CI44Uji2g4aVsUYU9vz+bawhSJ8vdY3gTp4kmWvXCoxn+fNHjqTcb6WbuPPnhf/Lp3v8+T/IR8jKyFADgHDoEIn+C79irCF+T79aI8TPJyLKa92aqFEjYePr1yS3fXvBnNc32wXNm5PA0VHYEBNDMhs2FJqT4TiOuGbNSODqKrT/82eSCQkpcE9BOjpU08GBVv/5J31MSqI6derQ8gULaExGBgnmz//+XM+dE/p++3ay6NKFZGxt6e+//yYiomMnTtCxEycoP3kzZ/JjDQAUuGIFvXnxgmQ4jmx0dGhf167knZPDa0FR+xH/lZxlUfMRuSNHFvD9j6jwCfMvX75QXl4eaWtrS7Rra2sXWA4nYtGiRTRnzpwC7R8+fKDUb18ulJWVSUNDgxITEyn+1SuKv3mTiIj09PRIj4Q148ST7lWrVqXKlSvT+/fvKfbbuUREVpmZpPTtd4sHiLa2NskIBHRT7Fydd++olpoa5eXl0adPn/h2juNIV1ubkpOT6fHjx8Lr374lpdRU0tHRofT0dImd0OVyckiHiGJjY+n9+/dU/c0bUvjyReKe0tO/DcwqVSLZ2Fh6MekFRUdHk0LSTlL88EHinj5+/Ei5ubkkkJMjR6d+ZGJiQurq6vRi3jxSVHojcU+ysrIUGxtLRET2TsIZ//b29pS3axfFnT8veU+6upSZmUlfvnyhhk36EuTlSUlJiawMX1HqpUsUHx/Pb0ShqKhIWlpalJKSQo0d25NASdjJql3lIdXQf08JCQn8PRERqampkZqaGrVp2ZYSv/m9itx1qlXlvcQ9idDU1KQxIyZQ5Js3lJeXR2pKt0kt57bEPYnui4hIJy+PsjMy6P79+6Ty+DGpx8by95SVlUVfvnzhz0158oTMDA3py5cv9Ck6mqrGxpJXLS+JexJ9tHG2bUWqdnZkaGhI7/7+m7ivBwvcU1xcHGVlZZGLjTtl1K1LdevWpepxcfRx9eoC91SpUiWKiYkhV2sXSjcyEsakkhIpKu6TuB8ioipVqpCioiI1MzSkNHNzIiJS+vSJrOTv8M+JjzE5OdLR0aE/7O3piaawIyL/+TPVij9e4J6IhH9Pozt1ohf6+hQXF0dyCQmk835/gXsS0btlK1L38aF79+5R9ufPVOObraJ7Ev97Sn32jGplZJCCggLdvn2baordV82aNSkvL4+8ankREVHL+vWpn6s7NWzYkJI+f6bU599rionuKS0tTfg3UqcOdXdqQVWqVCFTU1NKiZ1Q4J40NDQoISGB4qtVoy5OwllDenp6pJe1heJiYwvViLcyMuTl1Iqys7NJQUGBrFKPkxJAMTExBTVCT4/a29h8j7t3u3+sEdbWNL5Xr+8a8WEvrxGie+LvVUuLpk9aRO/fvxdqRNwBUvjyhRITE3+oEXp6evT06VNSeP26gEZ8/vyZ14jYmzfp5qCbZa4RaZcvS9zTv2mEjt47+vTpk8TfR1lohDjDBs2kbC0toUao3CP1zMgC9yS6pjCNEL+nr1+/8rH35eFDUlVUJENDQ4qJiSHu27letbzKXCNEf0/N6tb9qUYQEenq6tIfDRtKRyNe7ylwT6K/pxamprRi+HDKy8v7oUaI/p7SKlemlDt3qGHDhpScnEypYueKNCI9PZ28anmVuUaIKKpGZGdnU613+8tUI8TvSVwjenSfRnp6egX6Eenp6YVqhKgf8frNG1IU83H+v6f33/pIT8Y9KVONKG4/QllZmWJiYkicstAIEe5OncjMw4M+f/5Mn/7+m6qmXS1wTykpKRQbG/tDjRC/J1EfNj4qijIyMoQaQcQ/J9E7syw1orj9CDk5ORrZ1Yde1a5dZhqhoKBAty9epJovdxS4J9HfU4F+xA80IjExkdIVFCjh5k1eI8S1nOi7RvQy6SUVjSAqej8iOzub9D8cKnONKM5YIzY29ocaYW9vT3nZ2RQn9jzy98tjbt3iNSL4wDByjlOh2oq1+fNT8lLoecZzqqFQg97s5SinknBMqHTtDMk8uUW1FWtTNfnvicUP2R/oQ/YHeqf5njSq1iUiIoMHz8hZO5ne15ajZZcvUGJWDukoK9EYy/pUU/EFNVrbiNrV60/ynDzdf3Gf8gA6FP03RX2QJTs1O9LkMuifZy9pw+1blHJgP8koylKt2rVoiFcz6sBxdOHhBbqXeo/UZNWonlI9WtHEkkKiXlCnYUMpVzaPtIy1aEKvlvT01V16mu+eaijUICKiqH9S6drlBLqYfJF6pZhTy4dpBe6pnlI9stKy+qFGiBDvl8c/ekTp345ZKSmRIsA/J5GmlKVGFLcf4eXYhGr260cPHjwoW40ozlhjQuH9iOLmI0aYjShzjSAqXj9C48NeUk5KKlONEN1TUccaHz58+KFGWBEVyLGI98sf3779vR/x6RPVIJIYPxF9f+c+f/78ez/i6VOqla9vJEJTU5MqEdGdO3eE/YgnT0itEN3jn9UP8hE1a9akpKQkSkv7/rf9K8Ya4vf0s36ExD0VQSNEiP6eHkdHU9q3metKnz6RFZFEjuVVwivKFGTSo/RH9PbrMar2tgMREakkJJPhvTMFdI+IKD4nnsI/ryOFZ63ISNmIlJNTye7hdQndU5MVfrhuUU2Ocn1q0e7aKtRRqyPlZaXT5WPCybjPM55TSl4KNatmRS283YlSY+jwpcM0/+U8+tr+Ky3tM4saH/k+s/xe6j2Sl5EnM2UzuvzwMn36+pKiMh7S1pStVHtIDQq9WJM/NzPrPa28coKqyVej2oq16UviM4oWvKKYrBg6lnCMFt1SLHBPb7LeUG3F2sTFGVGU7HsiIrqTeociyLrC5SyLmo94ducOFRUO+dP+FYzY2FjS1dWlK1eukKPoyxIRLViwgLZt28Y/LHEKm2Gur69P8R8+kJqa8I9ERkZG+MVKIBB+mfu2vEFGRoZkcnMLbKggOj9PICCILYWQzcsjjuMkhIPo20wIjqM8GbEvtzk5JPvt3/l32ZWTlyfIyX1vz8kh7tvvEX2VEcHJyJBspUrf278tWZC4J7HzZSpV4r/WIDubX97A35P4vSoo8DPDcjMzJZYfiZLbvI3fPj7IysoS5eZSXr4lL3JycsIvtHl5/PIjjuNIFqDszEy6ffs22djY8P89/l6/+Y6ISEYgIBmigvcksl1GhvinlJdHMt/8kP/5ycrKEqegQLki278tPypwT6LzFRWJRF+4xJYqSdyT6HnIy5OsvLzQxpyc78ulxO9JbLmUjJyc8Dnl5AjPz39PItu/LYGSkRF+z8/Lyip4T6LYE1t+JMtxwuchZmNeXh7duXOH7OzsiJOTk1jWJEdU8J5EtnMcCUTLxgQC4r75rNDnIS8vPF8g4JdAFRpjRCQjL08y8vLCdlEM57+n779c+DyIKC83V2JpVYHn922pkpycHEEgoLzMzIL3JLL927l8e2ZmoTFWEo3I5Ti6ffs22draksK3/zbTiOJphCA3V/Ke/kUjBLm5dPPmTV5PJGxnGlEkjZB4fsJ/CBsL0QiRntjb2/N/96JzmUaUTT9C9DHAtkEDUvi2vJFpRNE1orj9CMG3wYO4pjCNKLpGSNhehH4ErykODkKdYBpRpmON7OxsXk/kZGUrpEaYLqtPchDOjhchmuUrQzIkkPuuEVxuHgF5JEuyJMN992Ue8oSzWeXkiPvWLiMAUV4eP1NWfEZzLnIpWxYkL6PAnysrELbLcXLUW6c37f60m3IhfI4ZXA6RjAzJcXISpZhyIJz9K5rlS0SUIwPK4fJIhmRIHjL8ueL3JLrXXBmiPA6UR3kkLxCen/+e5Dg5ujv67m+jEUTF60fk5eXR7Xv3qGHjxsKZjEwjvjeWwVgjJyeH7ty4QTbW1iQvL/9baERZ9yPKcqxBRAX6KBWtH2Gz2ub7ygwZGeJkhfHOCUCygoK6R0QkgICyZfKIk5ETajmEJZPEdU9cs7O5PMqVgbAdxJdXEq1ukOeE/W05To666fSgLXE7KBvZJE9yEqWY8mu2qBRTDnKIA5GS4Pvfan7NFp0ralfM5QrcUx4J308kI8OXeMpDHj0bE/XbaMSv7kekZGZStWrVKDk5mc/v/ogKP8NcU1OTZGVlC8wm//z5c4FZ5yIUFRVJ8dtLTxw5ZWWSU1aWaJOhQnZOVVDIt6DpOwXavwWinHjNHfHDhZxboP0bHAlFPf+5hdpI34NHopbRv5wvKytL9O1LqUR7oZYTyf1gM5lCg05enuTEaxR9g/vB+TKVKgn9rKT0/Z7pX+71B+0StouLQmE2UuH+pR/YyJ+f79wf3pOMDMkUEnc/vKdvg738/DD2Cnl2RD+IPVlZSRtzc4VL0BQVJfwt4of3RMV7HjIk9AMRSdSs+uE9iV6s+fzww78nefkC5xL94O9JRqbA3/u/2l6pUtHv9ScagdxcvoPCMY0QUlyNkJMr8vMQyMgUqidETCOIiqgR4ucX0iZxT9/0hBQUivWc+HamEfmMK5pGcBxHspUqfdeUQs5lGvFr+hGCbzFemKYwjSiCRojbSEV4HiJNkZX9HpNETCN4435tP4KfqFKpEsmKEnSF2Ui/r0bkyXKURyCi/LVuOSJRuyjHICtszyEBEQkKOT+PCHn5/mMc5VBhdX+FSRL+XFEpXY6jPDlZypDNoxzk0vcTIDxf7Fz6ltDJLmC7sBxFFifI51Cxe8pHjozg233ls5HyCo3t/1uNyN/+M43IzeXflUwjyn6sIfvNv7JKSiTzE035f9EIorLtRxRolzildP2I3H/po1SUfkS6rKROUgFd/ZHuCTU775tm54jpauGaTfxHzJx8uprz7XfLcxwJZGVItCdCDuUWOJcon2aLtk/gxO9F/Hwx28WmP2fJFX5P/PtJfKr0b6QRv7ofIfdvdezzUeET5goKCmRnZ0cnT54kb29vvv3kyZPUsWPHcrSMwWAwGAwGg8FgMBgMBoPBYPwKXkx6Ud4m8OR+W8k8pduUQiceMv6/+U88sQkTJlCfPn3I3t6eHB0dKSwsjN6+fUvDhg37+cWM/0s4jqMqVarwGz8wyhbmb+nC/C1dmL+lC/O39GE+ly7M39KF+Vu6/Bf8/f+UbCESLmt/+vQpTTWaypckYJQd/4UY/3+C+Vu6MH9LF+bv35sKX8NcxNq1a2np0qX04cMHsrCwoBUrVpCrq2uRrk1JSaEqVaoUqcYNg8FgMBgMBoPBYDAYDAaDwWAw/n8oTn63sLIvFZIRI0bQ69evKSsri27dulXkZDnj/xOBQEDv37+XKPTPKDuYv6UL87d0Yf6WLszf0of5XLowf0sX5m/pwvwtfZjPpQvzt3Rh/pYuzN/Shfn79+Y/kzBnVCyY8EgX5m/pwvwtXZi/pQvzt/RhPpcuzN/ShflbujB/Sx/mc+nC/C1dmL+lC/O3dGH+/r1hCXMGg8FgMBgMBoPBYDAYDAaDwWAwiCXMGQwGg8FgMBgMBoPBYDAYDAaDwSAiljBn/KbIyMiQlpYWyciwEJYGzN/ShflbujB/Sxfmb+nDfC5dmL+lC/O3dGH+lj7M59KF+Vu6MH9LF+Zv6cL8/XvDAUB5G/H/TnF2UWUwGAwGg8FgMBgMBoPBYDAYDMb/D8XJ77LPHIzfEoFAQC9evGCbJ0gJ5m/pwvwtXZi/pQvzt/RhPpcuzN/ShflbujB/Sx/mc+nC/C1dmL+lC/O3dGH+/r1hCXPGb4lAIKC4uDgmPFKC+Vu6MH9LF+Zv6cL8LX2Yz6UL87d0Yf6WLszf0of5XLowf0sX5m/pwvwtXZi/f29YwpzBYDAYDAaDwWAwGAwGg8FgMBgMIpIrbwN+B0Rl3lNSUsrZEoaI3NxcSktLo5SUFJKTY2Fc1jB/Sxfmb+nC/C1dmL+lD/O5dGH+li7M39KF+Vv6MJ9LF+Zv6cL8LV2Yv6UL8/f/H6K8blG282RPrAh8/fqViIj09fXL2RIGg8FgMBgMBoPBYDAYDAaDwWCUhK9fv1KVKlX+9RwORUmr/8cRCAQUGxtLqqqqxHFceZvDIOFXIX19fXr37t1Pd7ZllB7mb+nC/C1dmL+lC/O39GE+ly7M39KF+Vu6MH9LH+Zz6cL8LV2Yv6UL87d0Yf7+/wMAff36lWrWrEkyMv9epZzNMC8CMjIypKenV95mMApBTU2NCY8UYf6WLszf0oX5W7owf0sf5nPpwvwtXZi/pQvzt/RhPpcuzN/ShflbujB/Sxfm7/8vfjazXATb9JPBYDAYDAaDwWAwGAwGg8FgMBgMYglzBoPBYDAYDAaDwWAwGAwGg8FgMIiIJcwZvymKioo0a9YsUlRULG9T/hMwf0sX5m/pwvwtXZi/pQ/zuXRh/pYuzN/Shflb+jCfSxfmb+nC/C1dmL+lC/P37w3b9JPBYDAYDAaDwWAwGAwGg8FgMBgMYjPMGQwGg8FgMBgMBoPBYDAYDAaDwSAiljBnMBgMBoPBYDAYDAaDwWAwGAwGg4hYwpzBYDAYDAaDwWAwGAwGg8FgMBgMImIJcwaDwWAwGAwGg8FgMBgMBoPBKBJsO8iKD0uYMxgMBoPBYDAYDMZ/mLS0tPI24T8JS7hIhxcvXpS3CQxGmZGenl7eJvyn2LdvHxERcRzHNLyCwxLmDAaDwWAwGAwG4/8KgUBQ3ib8Zxg+fDgFBQVRYmJieZvyn+HPP/+kx48fs4SLFJgzZw717t2bbt26Vd6m/GfIyckpbxP+M3Tq1InmzJlDycnJ5W3Kf4Jdu3bR4MGDaeHChUTEkuYVHZYwZ/xfwkSHwWCUFJZkKV+YfpctP4rvvLw8KVvy34HFtPTp2rUrjRs3jsW1lEhNTaV169bRzp07WdJcCpw9e5Y2btxIs2fPpufPn7OESxlTr149UlNTozlz5lBkZGR5m1Ph6dGjB/Xp04cyMzPL25T/BI0bN6Zly5bRmjVrKCkpqbzNqfA4OjrSqFGjaPv27TRv3jwiYknzigxLmDP+L8ifAOA4jojYILWs+FHChfm7bCjMr8zXZYNAICAZGeGrbf369TRjxgzq1q0bXbhwgeLi4srZuorJj/Sb8esRj+8DBw5QUFAQBQQEUGxsLMnKypazdRUTgUDAx3R2djZ9/fq1nC36b9CqVSsKCQmh2bNnU25ubnmbU2ER9UW2bdtG7dq1o8DAQNqxYwclJCSUs2UVm2bNmtGUKVPow4cPNH36dHr27BlLuJQhvXr1olGjRpGsrCzNnTuX7t69W94mVWi6detGhw8fpgkTJrCkeRmTl5dHU6ZMoaCgIPLz86OQkBCKj48vb7MqLLm5uVSnTh0aO3Ys9erVi3bt2kWBgYFExJLmFRW58jaAwRBPABw8eJBevHhB8vLy5OLiQtbW1gSAJWB+IeL+Pnz4MCUnJ1NSUhINHjyYFBQUytm6ioe4v2NjYyktLY3q16/PH2fx/WsR+XrKlCm0bds26t69OxER+fj40KBBg2jGjBmkpKRUniZWKMTjOyIigh4/fkxycnLk6OhIbm5u5WtcBUQ8vvft20cGBgYkLy9PU6dOpdOnT1OzZs3K2cKKhXh8L1q0iG7cuEE3btyggQMHUpMmTahNmzblbGHFJC8vjwYPHkyVK1emvn37kqysLPn6+pKiomJ5m1bhEO+D+Pr60qNHj2jNmjVERNSnTx+qUqVKeZpXIcnOziYFBQXq27cvpaam0v79+2nmzJm0ePFiqlOnDusX/kLENVxNTY00NTXpr7/+ounTp9OiRYvIysqqnC2seOTl5ZGXlxft27ePOnXqRLKysrRo0SJSUVEpb9MqHAKBgJ8s0atXL7px4wbNnz+fZGVlafDgwaSurl6+BlYwAJCcnDB9+s8//9C7d+/o8+fP5O/vTzk5OTR58mQ+ac40vOLAZpgzyh1RR2by5Mk0btw4OnbsGF24cIFsbW3pxIkTTHB+MeIJl9GjR9O6desoJCSEzMzM6NKlS+zL6C8EAO/vWbNmkaenJ7m6upKTkxOtXr2akpKSWHyXAUePHqWIiAg6evQorVixgsaOHUufP38mKysrliz/xYjryaRJk+j27dv0+PFjcnd3p127dpWzdRWTnTt30tatWyk8PJxOnTpFQ4cOJSKSmBHKdPzXIIrvGTNm0IoVK6hjx44UEBBAe/fupQULFlBMTEw5W1jxyMvL4xMAtra2NHr0aJo7dy4tX76czTQvA0QxPnbsWOrWrRvJyspSdnY2+fr60rZt29jy/l8MAH5yyvLlyykyMpJev35NERERNG3aNHrx4gWbpfgLEcX3+PHjaciQIVS5cmVq1qwZ3b59m2bMmMFqmv9ixPVbR0eHJk6cSGvWrKG5c+dSRkZGOVtX8RCPbycnJwJA9evXp2nTprHyLGWAaMzu5+dHEyZMoIYNG9KiRYvIxcWFwsLCaMGCBfx5TMMrEGAw/g/YvXs3atSogevXrwMAtm3bBo7jsHXr1nK2rGKyYcMGVK9eHXfv3gUAHDx4EBzH4dixY/w5AoGgvMyrcCxYsADa2to4dOgQ0tPT4eTkhPr16+P+/fvlbVqFZNeuXWjTpg0AYMeOHVBVVcXatWsBAKmpqXjw4AHy8vLK08QKxV9//QVdXV1cu3YNgND/HMdh48aN5WxZxWTRokUYP348ACAiIgIqKioIDQ0FACQnJyM5Obk8zatwREVFwdLSEhcuXAAAXLp0CQoKCti8eTMAIDc3tzzNq7BMnjwZxsbGGDhwIGxsbMBxHPz8/JCTk1PeplU4IiIioK6ujrt37+Lr168AgAEDBkBbWxurV69GUlJSOVtY8Vi6dClUVVVx5MgR3L17F7NmzYKdnR18fHzw4sULAKwf/qu4evUqatSogYsXL/Jt27dvh6urKzw8PFhfvAyYPHkyDA0NMWrUKDg7O0NOTg7Dhw9HRkZGeZtW4fj777+hpqaGyMhI/v24dOlScByHBQsWICEhoZwtrFjExMTA1tZWIkf16tUrTJ48Gfr6+li+fHk5WscoC1jCnPF/wcKFCzFs2DAAwL59+6CiooKwsDAAwgTAmzdvytO8Coe/vz98fX0BCD9WqKmpITg4GIAwoSiCddZLh0AgQGJiIpo2bYpdu3YBAE6dOiWR4MrJyWEJl1JQWOJ7yZIlcHZ2xoULF6CmpoY1a9bwx3bs2IGxY8eyDuQvZMWKFejduzeAwvU7Ojq6PM37rSksvkeOHIk//vgDf//9t8THIAAIDg7GlClTkJWVJU0zKzSPHj2ChYUFgO8fKETvy7S0NBw4cAAfP34sTxMrHMeOHYOqqiquXLkCAEhJSUFwcDBkZWUxc+ZMZGdnl7OFFYt169bBzMwMCQkJEprTq1cvqKurY82aNYiLiytHCysOAoEAmZmZaNOmDaZOnSpxLCQkBAYGBujevTtevXrFn88oHdeuXUOVKlX4SVkiNmzYAEVFRXh6evJawyg9Z8+ehZqaGv+ROSMjA+Hh4ahUqRJGjBiBtLS0crawYhEeHg5jY2PEx8dL6MXcuXOhqKiIFStWMP3+hSQlJUFPTw+LFy+WaH/z5g3Mzc1RpUoVTJ8+vZysY5QFrCQLQ+oUtuFkamoqCQQC2r9/P/Xt25cCAgJo8ODBRET0999/U3BwMKWmpkrb1AoBClkSdOfOHUpLS6OLFy/S4MGDafHixTRs2DACQH/++ScFBAQQEdu8rySI+5vjOMrNzaW4uDhq06YNHTt2jLy8vCggIICGDBlCGRkZtHXrVnr16lU5Wvz7Il6b8vjx4/zS2t69e9PHjx+padOmtHz5choxYgQREWVmZtLOnTspJSWF1fUrIYXpd05ODnEcR+Hh4QX0+/jx4xQWFsaWhZYA8fi+c+cOv2lt06ZN6d69e+Tj40MLFiyg4cOHExFRSkoKHTlyhAQCAduPooQUFt9fv36lL1++0MqVKyXel0REd+/epa1bt9KHDx+kbWqFJiUlhWrUqEGWlpZERKSqqkrDhg2jJUuW0Pz582nVqlWUnZ1dzlb+/ojiPTc3lxITE0leXp5kZGQoPT2diIQ1zbOyssjf35/Onz9fnqZWGDiOI0VFRVJWVi5Q0mno0KHUtGlTOnz4MA0aNIjevn3L+uHFRLwPLvpZUVGRtLS06PXr10T0Pe4HDBhARkZGFBUVRQcOHJC2qRWW1NRUqlq1KllYWBARUaVKlahr1660du1aCg4OpoULF7IxfQkRxbR4nMvJydHLly/5Mp9ZWVlEROTl5UUyMjI0YcIEOnLkSLnY+7tTWJ9QVlaWmjRpQlFRURIaXqtWLXJwcCATExOKjY1lJVkqECxhzpAq4gmAyMhI/oVpYmJCJ06coD/++IMWLlzID0ZTUlJox44dlJeXxzYLKQECgYDvbEdFRfEJl969e9O5c+eoefPm9Oeff/IJl7S0NIqMjGS7a5cQcX8nJycTEZGmpibJy8tT9+7dqVu3brRixQo+vj9+/Ehbt26lBw8elJvNvysQqw8/bdo0mjhxIt28eZOSkpKoatWqNHbsWDI0NKQzZ87Qo0eP6MiRI+Tt7U1v376lsLAwVl+uBIjr96VLlygxMZGIiOrXr0/nzp2jfv360YIFC/j4Tk1NpU2bNlFeXh77QFFMxH3t5+dH/fr1o8jISAJA7dq1o3r16lH16tVJXl6eYmNj6e7du9S9e3eKiYmhRYsWERGrY15cxH2+ceNGCg4OJiKiRo0akYeHB40fP54mTJhAI0eOJCKijIwMWrhwIeXk5PCJXUbxKSxONTU16fnz5/To0SMi+j5obdasGVWqVIkmT55MmzdvlqaZFYL8g39Rf6V///6kpKREXbt2JSIiZWVlIiLKysqi3r170/jx48nLy0uqtlYUCku4EAnHPZcuXSrQ/zMyMiIrKytycHAgPT09aZhYYRDvg6elpfF9FGtra3JycqKxY8fSjRs3eJ3/+PEjmZiY0KxZs/j3JqN4FKbfenp69P79e7p+/brEOQ4ODqSurk4LFy7k36+MoiMe37m5ubxfvb29ycXFhXr06EExMTH85tjKyso0ZswY2rZtG/Xq1avc7P5dEe8TPn78mG7dukXx8fGkoqJCgwcPpgMHDtCaNWv4SW9paWmUnJxMgwcPpo0bN7JxZkVC6nPaGf9ZxJd5+vn5wcjICIcOHeLbevXqBUVFRWzZsgWPHj3CvXv30Lp1a9ja2vI1udjSxKIj7u8ZM2bA1dUVR44cgUAgwLNnz+Dh4YEGDRpg27ZtyMnJwcOHD+Hh4QE7OztWI7QEiPt7xYoVmDBhAl+KYufOndDT00Pr1q35c1JTU+Hh4QF3d3dWkqUUzJ07F1paWrhw4YLEMv2srCzs2LEDJiYmqFq1KmxsbODl5cWfw3xePMS1d8aMGahbty727t3Lx/3YsWPBcRyCgoJw48YNREZGolWrVrC2tmb6XQr8/Pygo6ODo0ePSpQRSk1NRe/evWFubg45OTk0atQIzZo1Y/FdQsRjc9KkSdDX10dgYCBfDu7evXto27YtVFRUsHTpUvj7+6NFixYwNzfnfc72RSg+4j4TlRES6YWnpydatGiB27dv8+e8fv0aI0aMwOHDh1k/pZiI+zo0NBT9+vVDt27dEBAQAAA4efIk9PX10bRpU1y4cAEXLlxAmzZt0LdvX/46pivFQ9znx48fx/79+7F9+3a+zcnJCWZmZrh8+TLi4uKQmZkJLy8vrFq1itckpitFQ1zDFy1aBDc3NxgaGsLLyws3b95EXl4e2rVrB21tbfj6+mLVqlVo1qwZ3NzceB8zXxcPcX+lpaUhNzcXmZmZAIAePXrA2dkZ586d48+JjY3FsGHDcPbsWabfxUTc16tXr0a3bt3QsWNHTJw4EQBw5coVNGvWDCYmJvj7779x+PBhtG7dmt/PCQDzeTHIP+YxNjZGnTp1ULduXUydOhXZ2dnYtWsXtLW10bRpU3h6eqJhw4Zo0KAB/55kY56KA0uYM6TOjBkzoK2tjRMnTuDz588Sx/r06QMzMzPIysqicePGcHNzYwmAUjJ9+nRoa2vj77//lki4REVFwcfHB7q6utDQ0IC1tTVcXV2Zv0vJpEmTUL16dezatQuvX78GAHz48AGzZ89GtWrV4Obmhq5du8LFxQWWlpbM36UgJiYGjRo1wt69e/l/nzt3DiNGjEBISAh/3r179/Dp0ye+88I6jSVn5syZ0NbWxpkzZxAfHy9xbPz48WjQoAGv3y1atGDxXQqePHkCExMTHD58GACQmJiI6OhorFq1iq/F+uHDB/zzzz+Ijo7mB1QsvkvO6tWrUb16ddy4caPAsS9fvmDatGmwt7dH27ZtMWbMGN7XzOfFRzwBsHLlSvTo0QOtW7fGjBkzkJycjCtXrqBNmzawsbHB9u3bceTIEbRu3RrNmzdnWl4KpkyZgpo1a2LSpEkICAgAx3GYPHkyvn79iuvXr6Nx48bQ0dGBvr4+HB0dWb34X8DUqVNRq1YtuLi4oHr16mjSpAlu3LiBtLQ0ODs7w8DAAAYGBjA3N0f9+vXZR+ZSMHPmTOjo6GDDhg148OABNDU14ezszI83J0+ejObNm8PS0hIdOnRgHzxLiLi/li1bhs6dO6NJkyYYN24c3r9/j4cPH6Jjx44wNzfHypUrERERgVatWsHV1ZXpdymYOnUqqlevjsWLF2PVqlVQVlZGx44dkZubiytXrqBHjx5QU1ND/fr14eLiwvS7lAQEBEBbWxunTp0CAHTv3h2ampq4du0aAODChQtYsmQJevbsiUmTJrExTwWFJcwZUuXFixewsLDA33//DQBISEhAdHQ0AgICEBkZCUA4g+jUqVMsAfALuHXrFurWrYvz588DEG6c9fTpU2zfvh2PHz8GIEzK7N69G5GRkczfpWTHjh3Q09PDrVu3+LasrCx+Q7jz58+je/fuGDVqFJYsWcKSLaUkLS0NjRs3xvjx43H69Gn4+PjA3t4ebm5u4DgOs2bNAiA56GSDopLz7t072NjYYN++fQCAuLg43L9/H/7+/jh9+jQA4OPHj7h69SpevnzJ9KSUREVFwcjICKdOncKZM2cwbNgwWFpaQk9PDxYWFtizZ0+Ba1h8l5ycnBz06tULM2bMAAA8fvwY27Ztg7OzM1q2bIl79+4BEG74lP86RsmZOnUqtLS0sHLlSixatAgGBgZo1qwZAOGs56FDh0JRURGmpqZwdnbmB6QsmVh8Ll++DENDQ34zvmPHjkFBQYHfpFnE/fv3WR/8FxEWFgYdHR3cuXMHALB161ZwHIcTJ07w5+zduxerV6/GqlWreF+zhEvxefXqFaytrXH06FEAwMWLF6GsrFwgvtPT05GcnMwSt7+AadOmQVNTE+vWrUNQUBBMTU1hZWUFQKg3U6dORZUqVWBpaSkxCY7pd/G5c+cOzMzMcPHiRQDAwYMHoaqqitWrV0uc9+zZM8TExDD9LgUCgQDZ2dlo37491q5dCwA4fPgw1NTU+AlZolVx+WH+rniwhDlDqjx8+BBaWlq4ePEizp07h2HDhsHKygrVq1eHhYWFRIkWESwBUHLu3bsHS0tLnDt3DtevX8eoUaNgbGwMAwMD1KlTR6LDLoL5u+QsXLgQzZs3ByBMtgQGBsLU1BTa2tpYuHBhodewQVHRKCwuU1NTMWPGDDRq1AhycnKYOHEiPwtg4MCBGD58uLTNrNA8e/YM1apVw6FDh3D69GkMHDgQtra20NPTg4mJCXbu3FngGqYnReNHg0c7OzsYGRlBTk4Oo0ePxuHDh/Hp0ydYWVkVGCQxSs+YMWNQr149rFy5Es7Ozmjbti3GjRsHJycnWFhYIC8vT+JZsUF/6bhz5w7Mzc1x+fJlAMChQ4egqqqK4OBgifPevHmD2NhYlgAoJX/99RecnJwAAPv374eKigo/+E9KSsLJkycLXMP6KKVj0qRJmDp1KgBg165dqFKlCp+ASUlJKfQa5vOSER0dDRMTEwDCZKKKigqvJV+/fsWOHTv4kiEiWB+l5ERFRcHKyopP4B4+fLhQ/f78+TO+fPnCPlCUkmPHjsHY2BgAcODAAQn9Tk5OZn3wUpK/P5eSkgJra2s8fvwY586dk/B3ZmYmgoKCcPPmzfIwlSFl2KafjDIDhWx0YG5uTpaWltS1a1dq27YtKSgo0IIFC+j9+/eUm5tLT548KXCNaMMFxr9TmL/V1dVJIBDQtGnTyNnZmfLy8mjx4sX0zz//kLq6OsXGxha4hvm7+Ig2ddLR0aH3799Tt27dqFOnTnT9+nXq3r07+fr6kp+fHz169KjADueysrLlZvfvgvjGK7du3aJr165RdHQ0Va5cmaZPn0579+6le/fu0bJly6h58+ZERPT06VOqVq1aeZr9W1OYntSrV49atmxJ/fv3J09PT1JTU6MFCxbQu3fvSEVFhel3CRHfyOnFixf09u1bevv2LREJN8devHgxXbx4kVatWkXt2rWj6tWrs02wS8mPNuLr0qULOTk50ZIlS8jDw4MWLFhAK1asoDFjxlCNGjUoIyODf1ZEJPEzo/jEx8dTVlYWNWnShA4cOEA9e/akpUuX0rBhwyg1NZV2795NGRkZVKtWLapRowbJyMiQQCAgOTm58jb9/x7xGM/LyyMiIhUVFeI4jtasWUN9+/algIAAGjp0KBER3bx5k9auXUuvX7+W+D2sj1J08utKbm4u3b59m9TV1enWrVs0ePBgWrx4MQ0fPpwEAgEFBQXRli1bCvwe5vOfI+7rtLQ0IiLS0NCg3NxcGj16NPXp04eWLVvGb0L+6tUrCg0NpZs3b0r8HtZHKTlJSUmUmJhIzs7OdPDgQerevbuEfm/dupWSkpJIS0uLqlWrRhzHMf0uIuLxnZubS0REWlpaVLt2bVq1ahX17t2bli1bxut3VFQUHTp0iKKjoyV+D4vvoiHeDxe9A1VVVUlPT486d+5Mnp6eFBQUxPs7MTGRIiIi6N69e+VlMkOKMMVilAniCa6XL18SAKpcuTLp6OjQqVOnaN++faSnp0cODg78Ndra2lSpUqXyMvm3Rtzf7969IyUlJZKRkaFatWrRvn376O7du6SlpUUuLi4kJydHAPj/ZxQfcX+L07JlS0pMTKQzZ87Q+PHjqVmzZmRoaEg3btwgBwcHUlNT41/ILNFSNADwvvbz86Pt27eTgoICvXnzhhYtWkR9+/YlfX19IiJKTU2lZ8+eka+vLyUnJ9OsWbPK0/TfFvH4fvLkCWVnZ5O6ujrp6+vTrl276Pjx46SlpUW2trb8NaqqqiyJWwLEfT1nzhw6dOgQffr0iUxNTalv377Uu3dv8vb2JiJhUiA+Pp4fjIoSAYziIe7z/fv306dPnygnJ4cGDhxILi4u5OLiQl++fCFNTU3+mvXr11O1atWocuXK5WX2b4+43wEQx3GkrKxMtWvXpvXr19OECRMkEgB3796l48ePk5WVFZmamvK/hyUAfo64r/fs2UPKysrUtGlTqlWrFmVnZ9P48ePJz8+P15DMzExasWIFqaurU+3atcvT9N8WcZ9HRkZSnTp1SEtLi3r37k3+/v7k5+dHGzdupD/++IOIiNLT0+n8+fPUsGHD8jT7t0Tc10FBQRQbG0uDBg2iunXrkre3N4WGhlLnzp15LcnMzKTp06eTiooKOTo6lqfpvy3iPhf9rKysTAYGBrRmzRry9fWV0O+HDx/SyZMnydramtTV1fnfw/T754j7etOmTaSqqkrNmjWjGjVqUExMDI0bN47mz5/P+zojI4PmzZtHKioqZGJiUp6m/5aI+3v+/Pl08eJFGjduHLVt25bGjx9P48ePJ2NjY+rbty8BoJSUFBowYADl5eVR3759y9l6hlQop5ntjAqM+PIff39/2Nvbo0qVKvD29i6wTCs1NRUvX76Eh4cHLC0t2TKtEiDu7/nz58POzg5mZmZo3LgxX3NVdE56ejpiY2PRpk0b2NrasmWfJUDc36GhoRg5ciS8vb1x5swZvl18E6G0tDR4enqiZcuWbGlcKZg3bx50dHRw9uxZAMDIkSOhoKAAPz8/xMXFAQB27tyJTp06oVWrVmzjlRIiHqN+fn6wtraGqqoqPDw8sHjxYolzv379iujoaLRr1w4NGjRg+l0K/P39oampiSNHjuDKlSvo3LkzKlWqhI0bN/LnBAUFwdbWFk2bNmXxXULEl9xOnToV2traaNmyJbS0tODu7o7jx4/zfwMpKSk4cuQIv0Ecq71acsR1JSwsDOHh4UhNTUVqaiqMjY3BcRwCAgL4czIyMtC2bVt06dKFvTdLweTJk1GjRg2sX7+e30tl27ZtqFGjBvr06YOtW7di7969aNGihYSGM58XD3FN8PX1haOjI4KDg5GTk4MHDx6gY8eOMDU1xbFjxwAAz58/R9u2bWFvb8/em6Vg8uTJqF69OjZt2oRXr14BAO7evYuuXbvC0NAQI0aMwLRp09CsWTNYWFiwDT5LiLi/1qxZgw0bNiApKQkCgQAODg7gOE6i7KRIv728vJivS8HkyZOhra2NkJAQfPjwAYBwbzIVFRV4eXnhzz//xLZt2+Du7s70+xfg6+sLTU1N/P3333j58iUA4V5ZwcHBMDIygqGhIZo3bw4HBwfY2Niwfvh/CJYwZ5QZs2fP5hMAkZGR8PLygo6ODpYtW8afs23bNjRq1EhiIxAmPCXDz88P1atXR3h4OM6ePQsnJydoamri6tWrAIRJ3IULF8LV1RVOTk7M36Vk6tSpqFmzJgYMGIABAwZAUVERwcHBSExMBCD8GLRz5040b94c1tbWrKNeCp4+fQpPT0/89ddfAIR1WKtWrYrevXuD4zj4+fkhJSUFaWlpuHjxIqtz+wuYM2cOtLS0cPz4cTx9+hTdunVDtWrV+A0RAWDPnj1o3Lgx3N3dmZ6UgkuXLqFhw4YSG/GpqqqiZcuWqFy5MrZs2QJA+MFz27ZtvI9ZfJecwMBAiQ2a9+zZA47j4OrqiqNHj0IgEOD27dsYN24cevTowTZo/kVMnjwZOjo6WLFiBZ8AePz4MWrWrInmzZtj1apV2LBhA9zd3WFhYcESAKVAtNnkjRs3Cujyli1b4O3tDRUVFTRt2hRdu3ZlGv4LmDNnDqpVq4azZ8/iy5cvfPuZM2fQo0cPqKiooFatWrC0tGT98FJy6NAh6Ovr48qVKwWORUVFYdWqVbCxsUHnzp0xfvx4puG/AHH9jo2NBQDExMTAxMQEdnZ2WLRoEQIDA+Hu7g5zc3M27ikFmzZtgo6ODm7dusX7T/T/169fh7e3NwwMDODm5oY+ffrwvmbxXTLu3bsn8VET+P4hNDMzE0+fPoWvry9mzZqF0NBQ1g//j8ES5owy4fLly7CysuITAKdPn4aSkhJat24NAwMDrFq1CoBwhmJERAQTnlJy/vx5NGrUCOfPnwcg7Eiqq6vD0tISKioquHbtGgDhrBYm9KVn8+bNqFWrFr/Zx+XLl8FxHCpVqoSAgAAkJycjMTERixcvxoQJE1hHvZR8/PgRW7duRXp6Oi5fvgxdXV1eQwYNGgQlJSWMHj0aaWlp/DWsg15yIiMjYWtry8/mP3XqFJSVldGhQwcYGBhgzpw5AISD/MOHDzM9KSUxMTGYOXMmsrOzceLECWhrayM0NBQxMTFo2LAh5OTksHLlSolrWIKl6IgGPaL/T0xMxMSJE7Fu3ToAwN69e6Guro4lS5bA3Nwc1tbW/IbYHz9+ZBuV/SI2b94MbW1t3Llzp8Azef78OVq3bo0GDRrA1dUV/fr1YwmAUjJgwAAMGjRIoi2/L9+/f4/U1FQW47+Ad+/eoXHjxtizZw/fJj7zPC4uDlevXsXWrVtx7tw59t4sJQEBAXByckJWVhbflv+9mN+37L1Zcnbs2AEdHR3cuXOHbxPF95cvX+Dj4wMHBwe4u7tj6NChbNxTSiZOnIhu3bohLy+vQMIcEMZycnKyxLiH+brknDp1Cpqamnj69GmBYz/SDaYn/x1YDXNGmdCgQQPq0qUL2dnZ0alTp6hXr160evVqateuHXl6etLs2bMpISGBZs2aRV26dCEi4aZEbCOQkqGqqkqenp7k6upKJ06coEGDBtGCBQuoQ4cO1Lx5c+rYsSPt2rWLr6lNxPxdUjIyMigzM5NmzJhBdnZ2dOjQIerTpw/t3LmTXrx4QTNnziRFRUUaOnQojR8/nhQUFIiI+buonDhxgi5evEjZ2dnUoUMHcnJyIm1tbfL29iYlJSUKDw8nNzc3GjJkCBEJN3myt7enW7dukZKSEv97WJ3EkmNlZUU9e/YkGxsbOnPmDPXq1YtWrVpFPj4+1K5dO1q6dCl9+fKF34SSiMV3Ubl06RK9ePGCkpKSaOzYsUREVLNmTZo2bRrJy8vT1q1bqXfv3jRw4ECSlZUlExMTysnJoX/++YdGjx5NRML9D9imcEUnNTWVVFRUKDc3l+Tl5aly5crk7e1NRkZGFBUVRdOnT6fZs2fT2LFjqUGDBtSxY0eaPHkyhYSEUOPGjYmI+H0/GCXn8ePH5O7uTtbW1pSbm8vvo8JxHBkaGtKRI0coLS2NZGVl+VrxovMYRUe0wefTp0/J2tqab5OVlSU5OTnKzs6mmzdvkoWFBenq6vLXsRgvHQDo5cuXEj4U7VWTlZVFHMdR48aNeU0hYu/NkiCqN5yenk45OTkSx2RlZSkvL48OHDhA1tbW/HhH/DijZDx//pwcHR3JysqqgH5Xq1aN9uzZQxkZGSQjI0OKiopExPS7JIg2n3zw4AFVrly5QN34nJwcunnzJhkZGVG1atX465h+Fx1R3BJ996u8vDypqKhQXFwc1a9fX+LY3r17SV5enjp16iTxe5ie/HdgGQVGqbl48SJt2LCBli9fznfUVVVVacqUKaSsrEybN2+mfv36Ud++fUlHR4fMzc2pXr169OrVK4lNJ5nwFI3IyEjau3cvHThwgG+zsbGhESNGEBFRSEgI/fHHHzRixAjS0dEhIyMjAkDz5s0jIuJ9zvxdND5+/Ehv3ryh9PR0IiJSUlIiNzc3atu2Lb19+5ZmzpxJc+bMoe7du1OnTp1IRkaGxo4dSxEREXyyHADzdxFYv349devWjZ49e0bbtm2j6dOn87uVV65cmXJycujZs2ckLy9P8vLyBICePHlCS5cupcuXLxPHcWwj22Jy7tw5Wr16Nc2ZM4cSExOJiEheXp7GjRtHVapUoZ07d1KvXr3ojz/+IFVVVTI3Nydra2tKT08ngUDA/x4W3z9nw4YN5OPjQ8HBwTR16lRq06YNf0xZWZlSU1Pp5s2bVKlSJZKVlaXU1FTKyMggf39/Onr0KNsouARERERQ7969ydHRkRYsWEAxMTEkLy9Pjo6OpKWlRZGRkaSpqUk9evQgIqKUlBTy8fEhBwcHic34mO9Lz8OHDykuLo6IiOTk5CQSAJGRkZSZmUlqamp8spwlAIqGuA4TCbVYVlaWXF1dadeuXfT06VMJfY6JiaGNGzfSs2fPJK5jMV50RP0M8f5GZmYmKSoq0qdPn4jo+4cLImG/PTg4mFJTUyV+D3tv/pz88S1KIDZs2JAiIyNpz549EsfT0tJox44ddOXKFanZ+F/g6dOn9P79e+I4TkK/c3Nz6erVq5ScnExKSkp8spzpd9EoLL45jqMuXbrQ5cuX6ciRI3w7EVFsbCwFBgbS06dPJa5j+l00RB8kRIh+trW1JRkZGZo/fz59/PiRiIQ+z8rKou3bt9O1a9fKxV7G/wlSn9POqFCsX78eNWvWRMOGDVGjRg04ODhILBnKzs6GjY0NxowZA0BYg7Vbt27YsWNHgSW5jJ+zYcMG1KhRAzY2NuA4DqNGjZI4/uXLF9SrVw+bN28GICx506VLF1y+fJn5uQRs27YNjRs3hqamJry9vfkSQyKuXr0KS0tLfnPVBw8eYMaMGdiyZQtbGldMQkNDISsri/379wMAPnz4gMqVK+PkyZMS5wUGBoLjOHTo0AGWlpYwNzfnfc1ivHisW7cOOjo6cHZ2hoGBAYyNjZGSksIfFwgEcHJyQt++fQEAWVlZ8PHxwZYtW5h+F5OQkBDIyspi3759iIuLw6VLl6ChoYHHjx9LnDdp0iTUqlULkyZNgouLC+zt7flln8zXxSM0NBTKysqYO3cu+vTpgwYNGmDTpk0Avvty8eLFMDc3x927d5GYmIj27dvjzz//5H8HW3JbfH5UDmvTpk2oV68e9u3bJ9H+5s0btG/fHhcvXpSGeRUKcV9fvnwZp0+fRkxMDADg1atXcHNzg62tLR4+fIisrCx8/PgR7dq1Q5MmTVjZshIi7re4uDiJkiCTJk2CkpISTp8+zbelpaXBw8MD/fr1YxpeTMT9tWvXLqxatQq7d+9GamoqAGHZCnl5eQQGBuLGjRu4c+cOWrduDRsbG6bdJeRHurBv3z7UrVuXf4eKiI2NRZs2bXD8+HEpWFexEPf1+fPnsX//fnz48AF5eXl49eoVvLy84OzsjH379iEvLw8vXrxA+/bt4eDgwOK7lKxYsQI9evTAkCFDcObMGQDCOubVqlWDi4sLli1bhk2bNhXYT4Xx34QlzBklJiQkBHJycnwC4OTJk6hTpw6ePXvGnyMQCDB58mTY2tpi+PDhcHNzk+jIsM5j0RElXCIiIhAfH48DBw5AVlYWb968kTjP29sbOjo6WLlyJZydndGoUSPe32yAVHRCQ0NRqVIlrF69Grt374aRkRGGDBkicc7hw4chJyeHPXv24O7du2jXrh06d+7MH2cv2KLx119/geM4iUEmANjY2KB79+5wd3fH4MGD+Theu3Yt+vfvj3HjxvE+Zp3H4hEaGsrrd0pKCq5evQpDQ0M8fPhQ4rx58+bBysoK3bt3h6urKywtLZl+F5OIiAhwHCcxoPz48SPMzMwwc+ZM9OrVC3v27EFqaiqeP3+OiRMnwsnJCT169GCbwpWQdevWQV5eHgcOHODb2rZti9mzZyMhIQEfP34EIEwq1qxZE7Vr1+Y34xP5nFF8xPsY586dw4EDB/Du3TsAwjrlrVq1goeHB//R7cmTJ2jfvj0aN27MYrwUTJw4EXp6eqhUqRKcnZ35+vxXr16Fp6cnFBUVYWpqCjMzM9jZ2bHN+H4Bc+fOhbW1Ndzc3DB9+nS+vV+/fuA4DoMHD8bgwYPRtGlTiQ0Q2XuzaIj7aeLEidDS0oKxsTFMTU3h6enJf9xfsmQJNDQ0UL16dZibm6Np06bsvVlCxPXg+PHj2LlzJ98n/PDhA7y8vNC8eXMEBgYiPT0dDx48QPv27SXGmYziM2nSJGhqaqJatWrQ19dHcHAwsrOzcfv2bfTv3x/KysrQ1dWFsbExGjVqxPS7BIj7yt/fH5qamujduzfc3NygqqrKf8h/8+YN2rVrB2trazRs2BDdu3dnesJgCXNGydizZw84jsPBgwf5tvj4eBgZGWHKlCnw9vbGvn37kJaWhsePH2PixIlwc3OTSAAwoS86u3fvBsdx/KaegFDU7ezsEBAQAF9fX17sX7x4AR8fHzRs2BCdOnVi/i4BYWFhqFSpEj/bGRAmaXv27Ilr167h+vXrfPvQoUPBcRzq1KkjMRBlFI3c3FyEhISA4zgEBgby7Z06dUKNGjUQEBCAvn37onr16vDw8Ch08xv2YaJ4hIeHg+M47N27l2/LyMhA/fr1MXz4cLi7u2PDhg1ISEjAu3fvMGfOHHh6ekpsxMc6jkUjJSUFnTt3hqGhIcLDw/l2Ly8vaGlpYfDgwWjQoAEqV64ssbFnVlYW24ivhJw/fx4cxyE0NFSivUmTJrC0tIS+vj5q167NJ9Nfv36NDRs2YNOmTWyjsl/E5MmToaamBj09PSgpKSEsLAwAcP/+ffTs2RM6OjqoVq0aTE1NJRIATFeKhngy8cyZM7CxscGlS5dw+/Zt9OrVC40bN+b1RCAQICIiAqGhodi9ezfbbLKEiPc5NmzYgGrVqiEoKAj9+/eHjY0NunTpwh8PCgpCt27d0LFjR0ycOJHpSil49eoVvL29cf/+fSQnJ2PPnj1wcHCAm5sbkpKSAADR0dG4ffs2bt26xT8n5uuSM2XKFKioqMDQ0BAcx2Hx4sXIzc3F69evMWTIENSqVQuVK1eGqakpHB0dmX4XE3H9Pnv2LBo1aoTz588jLi4Ow4cPh6mpKZYuXYqMjAxkZWXh1q1b2Lx5M44dO8b0u5S8ePEC8+bNw5UrVwAIN2seM2YMOI5DREQEACAzMxPJyclISEhg/XAGAJYwZ5SAjIwMtGvXDkZGRhIJFy8vL9SoUQP9+/eHs7MzZGRksGbNGgBCoREIBEx4SkB8fDy8vb2hra2Na9eu8e0dO3aEhoYGevTogVq1akFXV5cflIquY/4uPk+fPgXHcQVmk7u4uMDAwACqqqqoWbMm2rdvzx+7fPkyrl27xjoyJSQjIwNr1qyBjIwMAgMD0atXL1hYWEisVpk2bRo0NTULlLBgFI+cnBz06NGjwNJaLy8v6OrqYtSoUejQoQM4jsP8+fMBFBwEsfguHvfv38cff/wBZ2dnREREoHv37rC0tMTz58/5c1xcXNCgQQOkpaVJXMtmIxaf9+/fw9TUFE2aNOFnknfp0gUGBgY4evQotm/fDm9vbygpKeHu3bsFrmeD/uIjHqeXLl2CnZ0dLly4gC9fvsDX1xdqampYvnw5BAIBkpOT8fTpU2zevBlnzpxh781ScODAAQwePFhihnN8fDyGDBkCBwcHLFu2rNDJEizGS87Ro0exdOlSfvyTkZGB7du3w9zcHF5eXvx56enpEtex+C4+W7Zsga2tLdq1a8eXYcnNzcXBgwfh4OCApk2b8klzcdgEoeIhrt+RkZFwcHDAlStXkJqaisDAQKioqGDmzJnIzMxERkYGPnz4gIiICFy9epXpdynYtm0bxo4di0mTJkm0jx8/nk+af/78ucB1TL9LxsGDB8FxHOrVqyexmvbTp08YM2YMZGRkJCbKiWD9cAZLmDNKxJs3b+Dt7Q13d3fs3bsXnTt3hpWVFV6+fMmf4+npCQMDA76TI4IJT/G5ePEievbsCSsrK1y/fh29e/eGubk5njx5AkA4i9HKygotW7ZERkaGxLXM38UjMTERfn5+UFBQwK5duwAAnTt3hrGxMW7fvo0HDx7gzz//hIqKClasWFHgetaRKRlZWVlYtWoVlJWVoaKiwg+CRPVBw8PDYWZmhrdv35anmRWChIQE9OnTB05OTti0aRO8vb0L6He/fv2gqamJL1++SFzL9KRkPHjwAL169YKenh6qV6/O+1WUIJ8/fz6cnZ2RnJxcnmZWGGJjY2Fubg4HBwd4eHjA0tISr1+/5o+fPHkSysrK/Iwixq8hMDAQvr6+BRIAfn5+qFKlClasWIH4+PgC17H3ZvFJSkqCq6srlJSU0KlTJ4lj8fHxGDp0KJydnTFz5kym27+Ia9euwcDAAOrq6jh27Bjfnp6eju3bt8PS0rLAswDYe7Mk5OTkYMWKFbC0tISBgYHEsdzcXBw6dAhNmjSBubl5gQ/NjJIREBCA0aNHY+TIkRLtq1atgoqKCvz9/fHhw4cC1zH9Lhlt2rQBx3Fo2bKlxF4IgDBpbmlpCT8/P9Yv/EXcu3cPAwcOhIKCAk6cOAHguzZ//vwZ48ePB8dxBfYrYzBYwpxRbERf7kUbNenq6qJmzZp8IiszMxOAsPats7OzxCZyjOIh3sm+cuUKunXrhqpVq0JXV5fvIIpespMmTULTpk0LzGphFJ+0tDTMnDkTHMfBwsIC9vb2EsnEt2/fombNmliwYEE5WlnxSEtLw7p16yAnJ4eAgAC+PTs7G23atEHnzp3ZwLOUiAY2CQkJ6NGjB3R1dVGjRg1+trNIT1asWAEHBwckJCSUm60VjaioKPTs2RMNGzbEjh07+Pbs7Gw0a9YMAwYMKEfrKh4xMTFo0qQJOI7DpUuXAHyP/+joaJiYmODUqVPlaWKFo1u3bnwCIP9kCX9/f1SrVg3z5s1jCYASUNi77927d+jSpQuMjY0LbMYXHx8PHx8fDBkyhL03fxFfvnzB0qVLUbNmTfTo0UPiWEZGBnbu3Inq1avD19e3nCz8fSksRtPS0rB+/XrUrl0bPj4+EiUPc3NzsWfPHon9bRilY+zYseA4Do0bNy7Q91u9ejXU1dUxYcKEQj96Mv6dH2nwgAEDoKenhw0bNhR4Zw4YMAC9e/dm+l0CfrTK5OHDh/Dx8UGVKlX4fqHIvx8+fEBgYCBbLcEoAEuYM0qESIjev38Pb29vODo6YufOnfzx3NxctGjRAv369SsvEysM+ZPm3bt3h4mJicQX0IyMDDRt2hQjRowoDxMrJKmpqVi0aBFkZWX55K14srFRo0YSJXAYv4bMzEysXr0aMjIyvN/btm0LExMTVo//FyHyX1JSEvr27Qt7e3uEhYVJ1KFs3bo1evbsyTrqvxjRTPMmTZrwSfN27drB3Nyc76Qzn/86YmJiYGVlVeCjp4eHB9zc3JiWlALxOBX347hx4yAnJ4ft27cXWPE2btw4tG7dmsV4MRH3b/6ZiK9fv0b79u3RrFkzbN26VeJYSkoKfy3zefH4kTbEx8fjzz//hKmpKUaNGiVxLD09HSdOnGAJ3GIi7uuYmBgkJCTwSdnU1FSEhITAzs4OPXv2lEhmiV/HfF48xPVA/OcFCxaA4zgEBQUVSOAuWrQILVu2ZFpSTMTjNDs7u8B7sWvXrjAzM8OWLVsKrJZg+l18xP197do1XL16la9ZDnyfvKKpqVkgaS6CJc0Z4rCEOaPEiATp7du36NChA5o2bconzT09PVkC4BdSWNLcwsKC3wS0bdu2aNCgAfP3LyYpKQmzZs0Cx3H87K28vDy0bdsW9vb2rINeRmRlZWH16tVQUFCAurq6RLKcdWJ+DSL9TkxMRI8ePeDo6IiwsDDk5OSgffv2MDMzY3pSRjx48AC9e/eGq6sr6tSpAyMjIxbfZUhMTAwsLCzQsGFDvH79Gp6enhI+ZzpefPInAPInVvr374/KlStj9+7d/KpDESI9YbpSNMR9Ldrjo1mzZti4cSPevXsHAHj58iWfNN++ffu//g7GzxH315EjRxAcHIydO3fyK2nj4uIQEBAACwsLjB49utDfwXSlaIj7ev78+XB0dISRkRG8vLxw+fJlAMDXr18REhICe3t79O7dm70nS4m4zzMyMpCYmChxfOrUqZCTk0NISMgP91Vh+l00xH0dEBCAzp07w8TEBMHBwRJ1tLt06QJzc3Ns27atwPuU6XfREY/LGTNmwMjICLVr10a9evUwZcoU/lhUVBR69eoFbW1tnDlzpjxMZfxGsIQ5o1SIJ807duwId3d3GBoassFoGVBY0tza2hpmZmbM32VIamoq/P39ISMjgy1btqBLly7M31IgKysLy5cvR6tWrVgysYwQ6XdCQgJ69uwJFxcX1K5dmyVwS0D+Ac3PBpMPHjxA27Zt0axZM+ZrKRAbGwsrKytwHAczMzPm81IgHutLly5Fhw4dYGxsjKVLl0ps1Ny/f3+oqqoiPDyc7a3yCxBtfL1w4UIMHjwYtra2GDBgAF69egVAmDTv2LEjLCwsJOprM4qHeGxOmTIFBgYGsLKygpubG2xtbfm9gz5//oxly5bBysoKffr0KS9zKwwzZsyAlpYWwsPD8ddff6F58+aoUaMGzp49C0CYNA8NDYW+vj5mz55dvsb+xojrt2jGeO3atTF16lSJBO7UqVMhLy+PsLAwfP36VeJ3MP0uPr6+vtDS0sLSpUsxd+5cGBgYoH///rh+/Tp/jo+PDzQ1NXH06NFytLRiMH/+fFSvXh0XLlxAQkICJk+eDI7jJFYFRUVFoW3btvDw8ChHSxm/Ayxhzig14klzV1dXuLi4sMFoGSHeSbl69Spat26NJk2aMH+XMampqfxM83r16jF/F5P8ycSizpZIT0/nY168diXj3wkODsb9+/eLdK74TPP27dvDzc2NxXcpEC/78TNev37N+5/5uniUZMD+9u1bjBkzhsX3L2L69OnQ0dHBggULEBwcDBUVFQwZMgSRkZH8OQMHDgTHcaxWfCnZsWMH6tWrh5s3bwIATpw4ARkZGZiZmaFXr1548+YNAODp06eYMmUK+5D/C1ixYgVq1qyJa9euAQCWLVsGjuNQu3ZtPrEYFxeHWbNmoU+fPmwWaCk4efIkbGxs+LIJ//zzD1RVVWFnZwcNDQ2+BGVKSgoOHDjA4vsXMGPGDOjo6GD58uXYu3cvqlSpgp49e0qU+/T19QXHcThw4EA5Wvr7s3fvXhgaGuLGjRsAhGVCOI6DoaEhevTogVu3bvHnzpgxg8V3KYmOjoaHhwf/4eHw4cNQV1fHoEGDUKlSJYlVQa9evWLazfgpLGHOKJTizpYTnf/lyxeWAChjxJ/Fw4cPmb9/AUXpnCQmJmLXrl28n5m/i4Z4vIaGhiIpKQnAz5PmbAZLyTh58iT09PQwdOhQREdHF+ka0bNITU1lelJMwsPDsXfvXgDAhAkT0KVLlwJLmAtDPP5ZZ714iJKDgND/+Ws6F4WSXMP4zoEDB1CvXj0+mXjz5k1wHAcNDQ34+Pjg9u3b/LkLFixgelJK9u7di5kzZwIQ+l5DQwPBwcH4888/oaamhr59++Lp06cS17CkS8n59OkTunXrxu8zceTIEaioqGDGjBlo2rQpDAwMeH8nJiby/RWm5SXj/v37mDp1KgDg6NGj0NLSQnBwMB48eIC6detCW1u7wKoJFt8l58iRIzAyMuJL3ty4cQNycnLQ0tJC69atJeo9r127lul3KTl16hS/J9OhQ4egrq6OLVu2YM+ePVBQUECfPn34EqsiWHyXnLS0NAQFBSEpKQkXL16Erq4ugoODAQg3UuU4Dr169ZK4hmk3499gCXNGAcRFIzw8HA8ePCjSdflfqEzsi0ZJRDp/MpElF4vOo0eP8OXLFwDCJc5FTSqKwzqPRUM8tt+9e4eqVavCyckJycnJBY6LIx7PZ86ckUi+MH7O+vXrYWdnhyFDhuDRo0dFuobpd/HJysrCuHHjwHEcvLy8ULlyZdy9e/en14nHd1FXAjCEnDt3Dq6urjh27Bjve1Fd4X+DDYZ+HQKBACdPnkRQUBAAYfJFXV0dO3fuxNmzZ8FxHAYOHIiLFy9KXMfem0WjsP5cWloaPn78iLi4ODRq1AhLly4FIPzIWbduXejr62PWrFk/vJ5RfM6fP4+XL1/i7t27qF27NtasWQMAWL58OTiOg4KCAp4/f86fz/xeNP5tM1WBQIAOHTpg+vTpfHvbtm1Rq1YttGnTBgDz86/g4sWLfDwfPXoUVatWxY4dO/Dw4UPIy8uje/fuOHHihMQ1TL+LRmHx/eXLF3z+/Bnx8fFwdnbm9Ts3Nxf169dH9erVMX/+fGmbWiH40VhFNCli6tSp6Nu3Lz+RZdasWWjXrh3atGnD+oWMIiNDDIYYAEhGRhgWU6dOpUmTJtHOnTspLS3tp9fJyckREdHNmzeJiEhWVrZsja0ACAQC3t979uyhVatW0fTp0+ndu3eUnZ1dpN/x/PlzyszMLEszKwz3798nNzc32rVrF40cOZKWLFlCubm5P71OIBDwPyclJfGxzvh3RLE9e/ZsGjt2LOnr69OVK1fI3d2dUlJSSEZGRsK3REIt4TiOiIjWrl1LXbt2pby8PKnb/jsi8uXAgQNp2LBhFBkZSYGBgRQdHf2v14nr98WLF4mI6XdRUFBQoBUrVpCxsTEdOnSI5s2bR1ZWVv8ar+LxvWbNGrKzs6Pnz59Ly+TfHh0dHVJUVKShQ4fS5s2b6eHDh6Svr/9Tn4u0aPfu3bR27VppmVshyK/RHMeRlZUVderUiRITE2nx4sU0bdo06tGjBzVu3Jjq1q1LGzdupPPnz0tcx96bP0cgEPD6kJCQQHFxcUREpKysTNra2hQTE0MfPnygJk2aEBFRTEwMNWrUiObOnUv+/v5ERPz1jKKRk5PD/yyuI66urmRgYEBXr14lCwsL6tevHxER6enpUY8ePcjf35/q1KnDn8/8XjREWnz//n26ceMG738NDQ36/Pkz3b17l/drUlISqaioUGhoKP3zzz9ExPxcXPLrNxGRhYUFeXt7U0pKCi1dupQmTZpEPXv2JCMjIzIyMqLw8HA6d+6cxDVMv39OTk4OH98irSYiqlatGmlpaVFKSgp9/PiRjIyMiIjo48eP5OLiQsuXLydfX99ys/t3JDk5mYi+j1UOHDhAq1atoqtXr1JCQgIpKChQTk4O3blzh5KSkkhZWZkyMjLo/v375OPjQ0ePHi10DMpgFEp5ZusZ/z/k/8q2fPlyVKtWDbdu3SqwW3N+xL/2r127FhzHISoqqkzsrKhMnjwZenp66Ny5Mxo3bgwdHR1s3bq10C/64v5euXIlateuXaQZdgwhs2fPRtWqVaGkpIRz584B+PfZh+L+3rBhAyZMmFBgAxzGj1m+fDnU1NRw4cIFREVFYf/+/TAxMYGVlVWBmebivg4JCYG6ujrCw8PLxe7fFfHZFmFhYbC1tf3Xmeb5fc5xHJvR/xPE9SItLQ19+/ZF9+7dISsri4iICABCv+afCSf+bEJCQqChoYE9e/ZIx+jfHIFAwPt91qxZUFRUhIODAw4fPsyfU5iOiz+D4OBgKCsr4/jx42VvcAVBfLPOV69e4dOnTxL9krdv38LCwoKvcRsXF4eRI0fiyJEjbJVKMdi6datEmSA/Pz/Y2NjAwMAAM2fO5GfHPXz4ENbW1pgyZQouXbqEdu3aoXPnzqwkSAnIX8ImLCwMo0aNwoIFC/DixQu+fd68eahatSo+fPiArKwseHl5wdfXlz/O4vznzJ49G0eOHOH/PXHiRNSqVQuVKlVC8+bNcejQId6P3bt3h5mZGVavXg03Nzc0adKEP8biu3iI6/fjx4/x7t07ifHLp0+fYGlpiZ07dwIAkpOTMXz4cJw5c4bFdTFYsmSJxL9nzJgBQ0NDGBkZwcfHh29/+PAhGjRogEmTJmHfvn3w8PBAq1ateP1mPi8aEydOxMiRI/H582cAwKRJk6CpqQlDQ0Po6elh7Nix/AbkO3fuhKysLFq0aAEbGxtYWlryfRi2WoVRVFjCnIH09HSJf2dnZ6N79+5YsGABAPxrRyV/skVDQ4NPGDCKxu7du6Grq8svzT9//jw4jsOhQ4cKnFuYv3ft2iU1W39XxJMt+/fvR5UqVaCjo4OVK1fiw4cP/3qdiNDQUMjLy7PNb4pBXl4e+vbtK7EreV5eHiIjI2FgYIDGjRsjJSUFgGRN4ZCQEKipqfG1oRn/zr8NIkNCQmBjY1No0jy/nlStWpX5/CeI+/rQoUOIjIxEdnY28vLyMGHCBImkuYj8H5BZfBcPcZ/n5OTg+vXrOH78ODw8PNC8efMf9jnErxN9gGM+Lxr+/v4S+uDn54c6derA3NwcXl5eEgncmjVrYty4cQgPD0fbtm3h6urKEgDF4PLly+A4DlOmTAEgnHhSs2ZNrFq1CnPnzoWysjJ69+6N+Ph4AMDMmTNhZmYGXV1dODk58ZvYssF/0Rk5ciSaNm2K69evAwDmzp2LypUro2vXrlBWVkarVq34Pnh0dDScnZ1RpUoVWFhYwNTUlCVcisHnz59Rs2ZNtGzZEufOncOhQ4dgamqKo0eP4vLly3B1dYWzszOftL1x4wZ8fHzQoEEDdOjQgY9vliwvOhMnTkRiYiL/7+nTp0NPTw/169eHu7s7YmJiAAg3KTcwMEC/fv2wceNGtGnTBo0bN2b6XQwePHgAjuPg7e0NQDimr1GjBrZu3YqVK1eiTp06aNKkCf88AgMDYWZmhnr16sHV1ZXpdwkYO3Ys7OzsMH36dJw+fRqtW7fG9evXkZubi8DAQDRu3BgDBgzAy5cvAQifSe/evTFp0iReu1lsM4oDS5j/xxkwYAC6d+8O4LtYp6enw9jYGOPHj+fPEz8mms2cf7YcSwCUjMDAQPTv3x8AsGPHDqipqWHt2rUAhPUpRfW2mb9LRv462llZWUhLS8Ps2bOhr6+PJUuW4OPHjwWuE3VigO/+3rdvn1Rsrkh4eXnB2dm5QPucOXPAcRwcHBwkYjs4OBiqqqrM10VEPL737NmDOXPmYPny5fxmfIAwfm1tbSU2As2fTGR68nPEBzRTp06Fvr4+tm3bxieykpOTMXHiRMjLy2P79u1ITExEp06dMHDgQP7a4OBg9mGiGIjH6ZIlSzBgwADe39HR0WjdujWaN28uoRdr1qyRmFkXGhrK4rsY3L59G9WqVYO7uzsAYY1bbW1thIeHY+nSpWjUqBGMjIz4mYqbN2+Grq4uzMzM4OLiwhIAJSA8PByKiorw9/dHYGCgxEegK1euQEVFBd26deMnuIhqa7NNmkvG1atXYWxsDG9vbxw9ehRdu3blNzp89+4dnJ2d0aJFC/zzzz8AhB89V61ahcDAQJZwKQYiDXj9+jVsbW3h7e0NPz8/iRm5X758gYeHBxwdHSXiPi4ujr+exXfRiYqKgoGBAWxtbZGWloaLFy+iRo0a+Pvvv7F27Vq0aNECOjo6/Fj+yJEjMDQ0hJWVFZo1a8b0u5gIBAKcPXsWOjo66Nq1K7Zt24YtW7bwxx89egRjY2M0atSInyD06tUrvH79mul3MRGPyVmzZsHR0REDBgxA7969C1Q7cHBwwMCBA/mkufhx5m9GcWEJ8/8wAoGAnx0HfE8Qpqeno1+/fujYsSPevHkjcc3t27fRsWNHvH79mm9bs2YNSwCUANGLcujQofDx8cGVK1egqqrKJ8sBYNWqVfD395cQ99DQUFSpUoX5uwiIJ1vmzp0Ld3d3nDx5km+bMWMG9PX18eeff+LTp08AAB8fHzx+/Jg/JyQkhPm7CPxo9s++fftgYWGBdevWSbTv3LkTAwYMgLW1Nby8vAAAN2/eRO3atZmvS8CUKVOgra0Nb29vfuCzfv16/nhISAjs7e3h4+Mjod9BQUHQ0NBgPi8GCxcuhI6ODi5duiTxYU3E1KlTwXEcGjRoAFNTU/6c06dPg+M45usSMHnyZOjq6iI4OFhio72oqCi0adMGTZs2hb+/Pzw9PaGlpcUns8LCwqCkpMQ+wBWD7OxsHD16FObm5nB3d8e6deuwYcMGAMJ+461bt2Bra4t69erxSfPnz5/j3bt3LAFQCvbs2QMFBQXIyMjw2i0a5F+9ehWqqqro0aMHvwxdBEvcFg9RjN66dQv169fnV0WIT5x4/vw5nJ2d0bx5cxw9erTA72A+LzoiX7169QrW1tb8psDixMfHo127dnB2dsb69esl+pNsZnnxyMvLw/nz59G4cWPY2tpi7dq1/AafgDCB6+7ujurVq/N9wffv3+Pz58/sA0UpOHPmDHR1dcFxHFavXi1xLDo6GiYmJnB0dOQ/+Itg8V08xLXXz88P2traMDY25icXiggODoaTkxM6derEytYySg1LmP9Hyf/lOCwsDAYGBvzXzwMHDqBSpUqYOHEiPyMxLi4OHTp0QIsWLXiBv3z5MuTk5Fid4SLwo5filStXUKdOHXAcJ5HgSktLg6enp0Q5i71794LjOOzfv7/M7a1ITJkyBZqamjh8+HCBF+f06dNRp04d+Pj4wNXVFdWrV+cTXBs3boSKigpLcP0E8dj+559/sHXrVr4OdlxcHHr16gV3d3esWrUKeXl5+PTpE9q3b49Zs2Zhw4YNqFu3Ll8z9MGDB+VyD78zq1evRp06dXDjxg0AQj2Xl5eHra2txEDpzz//RP/+/fnnFRkZCSUlJVZHuxgkJSXBzc2NHxC9e/cOZ86cwcCBAzFz5kwkJSUBEJbWCg8P5zv3ubm5SEtLw82bN8vN9t+ViIgI1KhRgy+fAACZmZl4//49AGEiZuDAgXB1dUW7du14/X779i06duzI3pfFQNQ3zM7Oxj///AMbGxvIysoiODhY4pzbt2/Dzs4OxsbGfL9RBEsAFI3CZnAePHgQSkpKGDhwIL9KQnTetWvXwHEcZs+eLVU7KyKiGL158yZMTEygoqJSIDH+4sULNG3aFNbW1rhw4UJ5mPlbU1h8v337Fo0aNYKVlVWBvSTi4+Ph4OCAYcOGScvECof4Xgbnzp2Di4sLZGRkCtTYjo6ORvPmzVGjRg28evVK4hjT76KRP75zcnJw5swZGBoaomXLlgXOe/z4MdTV1TFkyBCp2llR+FFczp8/H4aGhpg8eXKBEqsBAQEYMmQIi2lGqWEJ8/8o+WdHXLx4EdbW1rC3t+c34tu+fTt0dXVha2sLS0tL2Nvbw8rKSqKeXGxsLO7evSt1+383xMX6wIEDWLp0KdauXYvLly8DAEaPHg1jY2PMmzcPCQkJuHbtGtq2bQtra2uJL/3p6ek4ffq01O3/nTl37hwMDQ0RGRkJQFgr+9OnTzhw4ADfkQkMDMSwYcMwYMAA3t8ZGRkYN24cDh48WG62/25MnToVlStXhpGRETiOw5w5c/jE1qBBg2BoaIiqVauifv36MDMzAyB8PnXq1CmwARfjx4jrSWZmJqZNm4aAgAAAwF9//QV1dXXMmjULnp6eqFevnsTsflHMCwQCfPnyhW3QXEwSExPh6uqKKVOmYNu2bejSpQuaNm0KJycnWFtbY/DgwQVmZ7HZiKVj0aJFaN26NQDg3r17WLZsGUxMTKCuro758+cDEJYvS0lJKVB7NS4urnyM/g0R1wZAqC1///03LCwsYG9vX+D8O3fuQE9PD926dZOqnRUBcQ1PTU2VOLZnzx7IyclhypQpBcojPHz4kM3+LCE/SprcuXMHRkZG6NChg0QpMwB48uQJhg8fzhIuxUTcX69fv8bXr1/51SgvX76EtbU1WrZsWWA8k5yczHxdQvLrd25uLk6fPg1HR0cYGhryY3sRjx8/hqWlJTw9PaVu6+9Ofv0W7cEkEAhw5swZaGlp8StnRe0A8ObNG9YfLAHi/r5w4QKuX7/O7/sGCCe+2drawtfXl18tLoJtiM34FbCE+X+Qs2fP8mUpBgwYgHHjxgEQJq7s7e1hbW3Nv1ivXr2K7du3Y+rUqVi/fj3fUWcd9pIxefJk6Ovro127dvDy8oK6ujoOHjyI2NhY+Pr6Qk9PD6qqqrC0tESLFi34wVJubi7zeQk5cuQI9PT0kJ6ejkePHmH69OkwNDSEqqqqRBJA3L9sk6GiIe6fW7duwdHREVevXkVGRgZCQ0OhoqKCKVOmID09Henp6Xj58iXWrFmDv/76i/f32LFj4eLigoSEhPK6jd+WrVu34t69e3j79i3ev3+Pp0+fwsjICMuXLwcAHD9+HGpqaqhbty52797NX8dqUxaNf5vRYmNjA2VlZcyYMYOffTh48GA2e6iUFBab+/fvB8dx6Nu3L+rXr4/u3bsjKCgIAQEB4DhOokQLIHxuLMaLh3isZ2Rk8EncrKwsHD16FPXr14ebm5uEXwUCAZ4+fcoSAMVE3IcBAQHo1KkTOnbsiPPnz/N+37Vr1w+T5gDrgxcXcd/t3bsXq1evxvHjx/ll/NevX0f9+vXh7e1dIGkugvUHi49og1pjY2OJFcvPnz+HlZUVWrZsiTNnzhS4jvm6eIj76+vXr/wGk7m5ubhw4QJsbW0lxvYi3rx5w3xdCubPnw8PDw80bNgQhw8f5jfDFiXNRRuB5oe9M4uOuHZPnDgR2tra0NLSQqNGjRAUFMQfEyXN/fz8EBsb+8PfwWCUBJYw/w8hEAiQmpoKCwsLuLm5oWvXrlBXV8edO3cACF+4Z8+e5ZPm+ZfZimBCXzLCw8NRs2ZNXL16FQCwbt06yMrKYvPmzQCEs7kSEhJw8uRJPHnyhNUCLQGFdfyePn0Ka2trmJiYoHr16hg0aBA2btyIV69eQUFBgZUTKgH5V5UsWbIEw4cPL7CUNiwsDCoqKpg2bVqBDszNmzcxbtw4qKmpsVUqRST/BohVqlRBVFQUrxE7duyAra0tXyPx8OHD6NChA5YvX84GRcVE3F979+5FUFAQ5s+fz8fxhw8f8OzZM4lrWrRowX+AZhQfcZ8nJiYiKyuLn7kVGhqKli1bYt26dfwS8levXqFx48ZsdcovZN68eWjVqhXs7Oz4d2Nubi6OHj0KU1NTfiPQ/LB+YdEQj/Fly5ZBTU0N06ZN4/c7WL16NZ/U2r17NypVqoShQ4cy/5YC8WTJpEmTUL16dRgYGMDU1BR//PEH3r17B0CYNDcyMkKXLl1YCZYSIh7f4eHh/GbB48aNg7u7O9q0acOX3Xvx4gVsbW1hY2ODW7dulZfJFYrZs2fD2dkZZmZmEvthXbhwAQ0bNoStrW2BpDnA9LuoiMf3n3/+iWrVqsHf3x8dOnSAkpISAgIC+Mk/Z86cgY6ODlxdXcvL3N8ece2+e/cuTE1NERkZiRMnTmDy5MnQ09PjV9cCwprmenp6CA0NLQ9zGRUYljD/D5KWlgZdXV3IysoiLCxM4pio7lmjRo0kyrMwik/+BNWiRYvQp08fAMKNEFVVVXlRT0lJKbR2M0tyFR1xX0VGRuL8+fP8x4n79+9j3rx5OHjwID/zIi4uDo0aNcLZs2fLwdrfl549e0rU1QeENeI5joO9vX2BEgjr1q2Duro6Ro4cKXFsy5Yt8Pb2llhWxyga0dHRmD17Nl+bWdSp3LVrF4yMjPDXX3/h69evaN++PXx9fQuUqWAUnQkTJkBLSwsuLi7Q1tZG/fr1ERwczC8vT0pKwo0bN9C2bVtYWFiwD5wlRHxgtHDhQrRo0QINGjRA7969+WSKqKZzXl4eMjIy0LZtWzRr1oy9J0uBuO8CAgKgra2NGTNmoFevXpCRkcGCBQuQm5vLJ80tLCzQoEGDcrT49yT/DLdHjx5h4MCBErNrBw0aBCsrK6xcuZLve2/cuBGurq5shlwJEffbvXv30L59e9y+fRtpaWlYu3YtXF1d4eXlxSfNb9y4ATU1Nfj6+paXyRWCo0ePYtKkSdi4cSPfFh4ejpYtW6JVq1b8eOfJkyfo27cv0/ASIu63wMBA6OjoYMGCBRg1ahRkZWUxYcIEpKenAxAmzRs3bgxdXV1+JjSjZDx79gzjx4/nV+sDwNy5c1GlShUsXbqUH2cePXoUHh4eLL5Lyfr169GvXz9MmjSJb3vz5g38/Pygq6uLZcuW8e2hoaFsrMP45bCE+X8EkVjn5ubi/fv3sLOzg6mpKVq2bFlg4xVR0lxPTw/9+/cvD3MrFIcPH0ZMTAwWLFiA8ePH48CBA1BRUeE30RIIBAgPD8fs2bPZB4oSIj4omjZtGszNzVGnTh3Y2dmhXbt2EudmZWXhw4cPaN++PRwcHNiLtZi8fPmSn/X5+vVrvn3ZsmXgOA5//vknn0wUERgYiBYtWhQY9P9oFQvjx5w7dw4cx0FZWbnAZrSPHz9GmzZtoKenh1q1asHS0rLQ5fyMorFv3z7UqFEDd+/e5WN+4MCBsLe3x9atWwEAf//9N9zc3ODp6SlRQotRMmbMmIFq1aohODgY06ZNQ4cOHaCsrIxz584BEC4337ZtG1xdXWFjY8PKZ/0inj59itmzZ+PEiRN8W3BwMDiOw7x58/ik+V9//YWePXuyGC8m4puN79y5E/r6+qhfv36BTYAHDx4MKysrrFq1ik+6iGAaXnJ27dqFFi1aoEuXLrxmAMIPEi4uLvD29uaT5lFRUSy+i4n4DPEbN27A2toaGhoa/ApaEREREWjVqhXatGnDbwwvgml4ybl//z4WL16Mw4cP82179+6FrKwsxo0bx39sPnnyJAYOHMjiu5g8evSI//nIkSPgOA41atSQeF8CwhVa6urqCAgI4Fd6imDxXXQOHDjAVz/49OkTevXqBQ0NDfTq1UvivLdv38LPzw+1atXCrFmzJI6xGGf8SljC/D+AuEifPHmST8rGx8fDzs4OzZo1w4kTJwp0xqOjo5nglACRyAPC+n26urp48eIFNm3aBGVlZSgoKPDJckCYNGzVqhUmTJhQDtZWLERL5K5evYqcnBzMmzcPHMfxs8izsrKwadMmNGvWDI0aNWIJrmIiPtAMCQmBo6OjxAy52bNnQ0ZGBitXriyQNBffkIgN/ItOYZ3sJUuWgOM4+Pv7F5jR/OTJE/zzzz/Ytm0bH9ds1nPJCAoKgp2dHVJTU3kfCgQCdOnSBTY2Nvx5kZGRrITWL+Ddu3ewtrbGvn37+DbRhsHVqlVDdHQ0EhISsG7dOkyYMIHtqVIKoqKieB0+e/YsOI6DhoYGjhw5InFeSEiIxExzcT1i782iERwcDH19fX4zsuzsbHTq1AkKCgoIDAxEZmamxPlDhw6Fjo4OKxdXCtasWcOv4BQIBPzeNUZGRgX0YtOmTXBzc4Orq6vEhnEsvovG2rVrwXEcnjx5ItFmbGyMpk2b4v379xLn7927FzY2Nnz5MpZILD53797ldeP69evgOA5KSkoFNGPfvn2Qk5OTmGkugsV30QgJCUG9evUkJghNnjwZHMdh6dKlBWbrL1iwABzHYceOHdI2tUIQGhoKjuMkymLdv38fgwYNgqqqKrZv3y5x/rt37zBmzBh07NiRjS8ZZQZLmFdw8s+8NTU1xapVq/ik+bt372Bra4uWLVvi8OHDyM7OhpOTE/z8/Pjr2Eu16KxduxZmZmZISkpCbGwsRowYgaNHj/LHx48fD47jsGvXLty5cwf3799Hq1atYGNjI5GQYRSN/L7q06cP1q9fDwA4ePAg1NTU+LJDos7iyZMnsWrVKpZsKQWJiYl49uwZjIyM0KFDB4myNrNmzYKsrCxWr15dYAY5i+2Ss337dly8eJH/9+zZsyErKyux5LkwmH4XH9EAfsmSJahfvz7fLtKQZ8+eQUlJCZcuXSr0OkbJePLkCRQVFSXemYBw9nPjxo35mqyijREBFt8lYe3atWjQoAFfCx4QlozjOA4BAQEFdDosLAwcxxWYLcr4OaGhoZCVleXLZ4l/WPP09ISlpSXCw8P5FSwiFi9ezGK7hIjiVXwFVk5ODpYtW4Z69ephyJAhSEpKkrgmKCgII0aMYBpeTEJCQqCoqCjxkVPE2rVr4ejoiL59+yImJkbi2NmzZ5mvS8jatWthZGQksYdKWFgY5OTk4OvrW0A3/vrrL3Ach5UrV0rb1N8eUfL2r7/+AiA5hhkxYgQqVaqE7du38zP4RWzevJmNLUtASEgI5OXlceDAgQLH7t+/j8GDB8PU1BS7du2SOPbp0yeJSVkMxq+GJcz/I/j7+6NatWq4dOkSP/NTJCpv375FkyZNYG5uDiMjIzRo0KBA553xc0TLl/fv388v2TIwMMC1a9ckzhs2bBj09fWhoqKCRo0aoVmzZmymcwkQ72zfv38f2dnZaNSoETZt2oRjx45BRUWFT7Dk5uZi6dKlBV7CzN9FY//+/XyHccKECRg0aBAA4TJFMzMztGvXTiJpPmfOHHAch4iIiHKwtuLx9etXaGpqwtnZWUJPZs6cCVlZWWzatKn8jKsA/Gjg/v79e1SpUgVDhw6VaL9x4waMjY0RHR0tDfMqJOKDGtHPGRkZcHNzw/jx4wuUJ2vcuDHbUPUXIEoAFJbg8vPzg5ycHF9uSJwDBw6wBEAxCQkJkUiWixDNxM3JyYGHhwesra0LTZoDrI9SXEQ+Lyzhkp2djYULF8LR0REjRowooDEiHWKJ3KKR/2OQiNOnT/M/BwUFwcnJqdCkOcB8XVxCQ0MhIyNTqH4HBgaC4zisWLGCbxPF9Llz55h+F5OQkBDIyckV8LX4KvLhw4dDSUmp0KQ5wCZkFYd169YVmixfuHAh//P9+/cxZMgQmJqaYvfu3QV+B0uWM8oKljCvgGzfvp3fpRkQ1hxu2LAhjh07BgD48OEDrl+/jgkTJvDLtz58+IAtW7YgODiYzbwtAXv27AHHcfyMw5SUFPTs2RMcxxUq6vfv38elS5fw8OFDtpS/BIi/FKdMmYKWLVvi5cuXGD16NNzd3VGlShWJsjcfPnxAu3btEBQUVB7m/tZ8/foVQ4YMgby8PLy9vaGsrCzRYfxR0nzDhg0spktIYZ2+9+/fw9TUFG5ubvxmtoAwaa6oqMhiu4SID9gPHDiAZcuWYcuWLbyPd+7cCVVVVX7zyZs3b8LT0xPOzs5ssF9CxP2WmZkpkbiaM2cOGjRogNDQUH4AmpaWhiZNmmDp0qVSt7UiIUoA5E9widdnnTFjxg+T5gDrpxSV3bt3g+M4nD9/XqK9R48eGDNmDL+MPycnB+3atYOdnR22bNnC/FsKwsLCUKlSpQLx3bdvX75vkpOTwyfNR48eXWCmOUu4FA3RrGXx5DgAeHl5wd7eXkLTg4KC4Orqivbt2xfYFJ5RdEJDQwtN4N69e5f/sLZy5UpwHIfly5cX+juYvhSNiIgIcBxXYI+3Tp06YfTo0RKr3EaMGAEVFRWEhYWxyYYl5Pz58+A4ji+jJaJLly6oXr06Pn78yLfdv38fw4YNQ9WqVSU2XWUwyhKWMK9ghIaGonXr1hID0i9fvqBOnTpYsmQJIiMj0bt3b1hZWaFRo0bgOK5APSiAzWopDuvWrQPHcZCRkZHYxObr16/o2LEjtLW1cePGDQA/7oyzxEvJiIqKQpMmTfgPFVevXuVn7otmccXGxsLDwwOOjo4srkvIly9fYGxsDI7jEBgYCEDY8RZ1vh89egQLCwu0b9+e/zAngnXQS45oMC/SjZiYGBgZGcHFxUVipvm4cePg4uLCBvulYNKkSdDR0UGjRo1gbm6OatWq8UnDw4cPw8DAADVq1EC9evXg4uLCNpssIeL+WrRoEVq2bIl69ephwIAB/Ptz5MiRMDc3R4sWLTBp0iQ4OzvD3NycaUkpEH3UP3jwoES7p6cnRo0aJVFH28/PD5UqVUJISIi0zawQxMfHw9vbG9ra2hI63blzZxgbG+PNmzcAIDE5pWHDhujXr1+52FsRePr0KTiOw5AhQyTau3TpAmNjY4lNV3NycrB48WIYGhrizz//lLapvz25ubkICQmR6A8CwmRigwYN+FrP4v3tpUuXYujQoex9WULCw8MLlBkCAA8PDwwYMEBidvOqVasgLy+POXPmSNvMCkFKSgo6d+4MQ0NDiVWyIv0Wxbd4f8THxwfNmjWTuq0VBdGEoCZNmvDJ8c6dO8PS0pL3d/5V5axsGUOasIR5BUQkIFeuXEFsbCwAYPr06TAwMICCggLGjRvHb+zUoUMHjBkzptxs/d0R1Y1btWoVhgwZAg0NDZw7d44/npaWBg8PD9SsWRORkZHlaGnFY+HChejYsSM6d+4ssenKqVOnUL16ddjZ2cHIyAiOjo6wt7dnZW+KiXjn5PPnz+jVqxc6d+6MKlWq8DNc8vLy+BkVjx8/RvXq1dnmtb+IZcuWoWnTpnjx4oVEe2xsLPT19eHq6oorV67w7ax+X8nZv38/NDU1ceXKFQgEArx8+RKzZs2CjIwMdu7cCUBYv/z27dt48OABWxX0C/Dz80O1atWwaNEiBAYGwtTUFM7Oznz98s2bN2PgwIHw8PDAqFGjmH6XgoyMDLRr1w5GRkYSCZcuXbrA1NRUopa5iJEjR8LV1ZXpSQm5ePEievbsCSsrK1y/fh29e/eGhYUF72uRX8U3ZmbJxJKTmJgIPz8/KCgo8PVtO3XqBAsLCz7hIh7LOTk5EhtjM4pHRkYG1qxZAxkZGQQGBqJXr16wsLDAy5cvAUiWtxH1EVnJm5KRk5ODHj16oG7duhL7SHTu3BlmZma8z8WZN28enJ2dmX6XkPv37+OPP/6As7MzIiIi0L17dzRo0KBAfIvrB4vr0hEbGwtzc3M4ODjAw8MDlpaW/Mdl8fGN+ApbgPUJGdKBJcwrECLREAgEOHPmDJSVlbFo0SKkpqYiPT0d0dHREqUU8vLy4OTkxJY5lxDRkq1Dhw4BAKKjo9GnT59Ck+bt2rWDvr4+Ll++XF7mVjg2b94MjuOgo6ODx48fA/j+Un348CF2796NBQsWYP/+/RKDUsbPEe/4HTt2DFFRUcjMzMSnT58wYsQIqKmpFVgWmp6ejo8fP7LOyy/i/v37UFJSgre3N580Fz2XiIgIyMjIoFGjRnjw4AF/DRsc/RxxH4n8Kfo4IU5iYiImTJgAGxsbidmJ+a9lFJ8XL17AxMSEf3cC31cCOTs748OHD3y7uGYz/S45b968gbe3N9zd3bF3715+9lb+BID4z+wjXPER99WVK1fQrVs3VK1aFbq6uvyHffF3ZKtWrSRqtjJdKTlpaWmYOXMmOI6DhYUF7OzsCo3v/BvysT5LycjKysKqVaugrKwMFRUVfkWcuD/d3Nwwa9Ys/t9MS0pGQkIC+vTpAycnJ2zatAne3t4/1G/RbHOm36XjwYMH6NWrF/T09FC9enV8+fIFgGQ/pEOHDvwqC4FAwPS7lMTExKBJkyYSJW7FcXV1Rbt27VhMM6QOS5hXEAoTjylTpvClWMTrP6WmpuL27dvw8PCAlZUVG4SWkK9fv+LMmTMSbY8fP/5h0tzBwQEdOnSQtpkVgh91Qvbv3w+O4zBq1Kif1kZkg6KiIa4lvr6+0NfXx44dO/jNgl++fIkRI0ZAXV2d3wOhY8eOEjPLma+LR/74FvnvwYMHUFNTQ4cOHSRmmoeHh2PAgAHo3r0783UxKcxfGzZsQI0aNfjZLCIOHz4MDQ0NtrnnL+bNmzeoVasWP5tcNHv806dPqFq1aqH1V9kAqeSI9OXNmzdo3749dHV1oaury38IEu8Ddu/eXaJMBfN78cmfNO/evTtMTExw4cIFvj0vLw+enp6oVasWH/+M0pOamopFixZBVlYWAQEBACQ1v3nz5qhTpw5LbP0i0tLSsG7dOsjJyfH+Br7Ht4GBAYvvUiKK34SEBPTo0QO6urqoUaMGnj9/DkBSv7t27YrZs2fz/2b6XTqioqLQs2dPNGzYEDt27ODbc3Nz4eHhASMjIxbfv5iYmBhYWVnB3t5eYvWEh4cHjI2Nmb8Z5QJLmFcAxF+Ie/fuldhkctq0adDX18eSJUvw6dMnAMCuXbvQsWNHuLu7s2XOJUAgEBTwl/i/nzx5gj/++AMaGhoSmyBmZmayTnoJEPdZVFQUrl69ivfv3/M1V7du3QqO4zBlyhR+BgDAOoqlZe7cudDW1salS5ckSt4AwhIto0ePBsdxaNCgAerXr886MSVEPL53796NefPmYfr06bh+/ToAYcyrqanBy8sLx48fx4cPH9ChQwesWbOGv47pd9E4ePAg+vTpAx8fH4mk4PXr12FtbY1Zs2YhJiaGb4+KioKZmZnEyixG8Xj9+jXu3LkjsQnc58+foaury888zMvL4wf9rVq1wrRp08rD1AqNSGfev38Pb29vODo68uWGAKGGtG3bFvXq1WNa/gsoLGluYWHBbwLatm1biWQLm7jy60hKSsKsWbPAcRw2bdoEQPg82rZtC1NTU97nrI/4a8jMzMTq1ashIyPDJ81ZfP9aRPqdlJSEvn37wt7eHmFhYRJj+LZt28LQ0JDp9y9GNNO8SZMmfNLc09NTInnL4vvXEhMTAwsLCzRs2BCvX7+Gp6cn0xNGucIS5r854smWu3fvwtTUFC1btpTYeM/X1xe1atXCkiVLkJKSgvj4eJw5c4aVqSgB4v5OTk6W+Ld4J+XJkyfo27cvtLS0CmyCyJLmRUd8QDN16lQYGRmhcuXKsLKyQteuXfkkjChpPm3aNHz+/Lm8zK0wxMfHw8XFBWFhYQCEnZdLly5h+PDhCAoKQkJCAgBhvfh169YxLfkFTJo0CbVr14aXlxd69eolsSFzdHQ0zM3NoaenB11dXdja2rJBUTEJDQ2Fmpoahg4dik6dOqFu3boSM4Zmz54NExMTjBkzBqdPn8aDBw/QqlUrODk5Mc0uIVu2bIGZmRm0tbVhaGiI06dP88fWr18POTk5rF+/nm/LycmBtbU1KxNXRoji+O3bt+jQoQOaNm3KJ83bt28vkQBgH+FKT2FJc2tra5iZmbHBfxmTmpoKf39/yMjIYMuWLejSpQvzeRmSlZWF1atXQ0FBAerq6jAxMWG+/sWI9DsxMRE9evSAo6MjwsLCkJOTgw4dOrAEbhny4MED9O7dG66urqhTpw7TEikQGxsLKysrcBwHMzMz5m9GucIS5hUEX19f9O/fHxYWFlBUVISLi4tEfdDp06ejTp068PPzQ0pKCt/OEgElY+HChXBwcICnpyeWLVvGt+dPmrdv3x5t27YtDxMrBKL4XLFiBTQ0NHDixAncv38fQUFBaNy4MVxdXfl43rlzJziOk5h5yyg+AoEAHz9+hJmZGZYsWYJ9+/ahR48ecHJygpWVFaytreHv7/+vqywYRUPU8du/fz9q1qyJGzduABCWAuE4TiKhGxsbi5MnT+LAgQPsA0UxESVn9+/fD0BY+sPJyQl79uzhSw0BQGBgINzd3cFxHCwtLeHo6MhrOntXFo+QkBAoKioiJCQEDx48QNOmTdGoUSP+eGpqKvz8/MBxHHr06IFRo0bB3d0dZmZmLK7LEPGkuWiloaGhIUsAlBHiSfOrV6+idevWaNKkCfO1FEhNTeVnmouvnGA+LxuysrKwfPlytGrVivm6jBDpd0JCAnr27AkXFxfUrl2b6XcJyN+n+9mKkwcPHqBt27Zo1qwZ83UJKMmKnrdv32LMmDHM34xyhyXMKwBr1qyBmpoaX6ri4sWLsLW1RZs2bXDkyBH+vFGjRsHb25stQywB4i/WNWvWQENDAwsXLoSPjw8sLCwwePBg/rh40vzt27cs0VICbt68yf+cmZlZoC5fTk4O/vnnH9jZ2WHGjBm8j0+ePMleqMXkR/E5Y8YM6OrqonLlypg2bRpfr79Lly4YOXKkNE2scJw4cUJCh1evXo3+/fsDEG7oqaKigtDQUADCJbivX78u8DvYB4qiceDAAXAch1WrVkm029jYwN7eHrVr10bLli1x69YtAEJ/37lzBw8fPuT/NpimFI8NGzZAQUFBov9x4sQJeHt74++//8bZs2f5PScOHjwIDw8PdOrUCcOGDeN9zeK77BBPmru6usLFxYUNSMsQca1nuvLrKIpGJCYmYteuXbyvmc+LRv5+YVHHMenp6Xy8s1VwRSc4OBj3798v0rniM83bt28PNzc3pt+lQLxO9s94/fo10+8SIL4/UHh4OLKysor9O0pyDYPxq+AAgBi/NYMGDaLExETat28f33b16lXq1asX1axZk6ZPn04eHh5ERJSXl0eysrIEgDiOKy+Tf1vOnj1LkZGRZGZmRp6enpSSkkK7du2iZcuWUdOmTWn9+vVERJSdnU0KCgr8dQKBgGRkZMrL7N+K0NBQGj58OEVHR5OxsTEREbVq1Yo0NDRo9+7dEucOHjyYXr9+TcePH5fwb25uLsnJyUnV7t8R8bg8ePAgxcfHU0pKCg0YMIDU1NTo6dOnBIB/DkTCZ2FnZ0eLFi0qL7N/axISEsjOzo4qVapEjx49Io7jaPbs2XT37l3q3bs3DRgwgJYsWULDhw8nIqJt27bRlStXaOnSpaSqqlrO1v9+HDx4kPr27UsDBgygMWPGUJ06dahz5850584d8vX1JVlZWVq0aBFVrVqVLl++TPLy8hLXM+0uHq9fvyYnJyeqU6cOXb58mW93d3en6OhokpOTo7y8PNLT06M9e/aQgYEB5eTkSPid6XfxyR+nP+vjic6Pj4+nqlWrkoyMDPN7ESmJJuR/HqwPXjyio6OpevXqVK1aNfL19aW+ffuSiYlJsX4Hi++iIR6bYWFh1K1bN6pSpcpP457FdMk4deoU9e/fn9q1a0fjxo0rUlyLnkVaWhopKSkx/S4GERERJCMjQ507d6aJEyfS27dvacuWLaSsrPyv14nHP+sXFp3z58+Tv78/TZ8+nY4dO0YrV66kN2/ekL6+/r9ex3zM+L+i3FL1jFIj+so5cuRItGnTBoDkhpQbNmyAsrIyvLy8cPLkSf46NsO8ZFy8eBF6enrQ1NTExYsX+fbk5GSEhobCyMhIYqY5o/iEhIRATk4Of/31F98mEAgwa9YsODg4IDIyUmKmS1BQEJydnSXKKjCKz4QJE6CtrQ1ra2vUqFEDtWrVwv79+5GRkQFAOOs2MjIS7dq1g4WFBZtZUQoEAgEuX74MCwsLWFtbQyAQ4MaNGzA1NUWlSpWwfPly/tyvX7/C09MTo0aNYrpdCiIiIqCnp4exY8eiVatWsLS0lJi1f+TIEXAcJ1Fjm1EyEhMTERYWBl1dXX4lio+PD0xMTHDnzh2kpKRg48aNqFmzJmbNmiWx4SfA+iclQfydGB4ejgcPHhTpuvw6zmb1/5z8mzSvXLkSvr6+ePv27b/OgBOP62fPniE9Pb1M7axI3Lt3D9WrV8fq1asxYsQIcBxXpBgXf1aJiYllaGHFQdxn7969Q9WqVeHk5MTvF/Sjmebi8X3mzBncvn27bA2tYKxfvx52dnYYMmQIHj16VKRrmH4Xn6ysLIwbNw4cx8HLywuVK1fG3bt3f3qdeHwXdSUAQ8jjx4/RsmVL1K5dG+rq6oiKigLw7/Eq7u9du3axUquMcoclzH8jftRRiYiIAMdx2LNnj0T7tm3b0K5dO9jb2/PL/Rkl5+3bt/D394eGhgbGjBkjcSw5ORlhYWFQU1PDokWLysnC35uNGzdCVlZWYhk/AHz+/BlxcXGoX78+2rRpg7NnzyIjIwPJyclwd3dHr169ysniisGuXbugpaWFe/fu8RvZdu7cGXXr1sWpU6cACGtqN2zYEG3atGGbwv0C8vLycOXKFRgbG8PBwQEA4OfnBy0tLSxYsAAPHjzAlStX0KZNG1hbW/MDI5ZMLB7i/tqzZw+0tbVRpUoVPq5FnDp1CiYmJnj48KG0TaxQiDQhOTkZmzZtQvXq1aGtrQ0bGxt8/PiRPy8vLw8NGjRgpZ1+AeIxPmXKFNSqVQu+vr5ITU0t8nWRkZFlZl9FZfLkydDT00Pnzp3RuHFj6OjoYOvWrYV+TBb39cqVK1G7dm28fftWmub+9syePRtVq1aFkpISzp07B+Dfy4SI+3zDhg2YMGECm1hRDGbNmoVOnTrB0tISHMfBzs7uh0lzcV+vWbMG1apVY5pSRMR9uW7dOtjY2BQpaS7u8wsXLpSZfRUVExMTyMjI8BNUipq8DQoKgry8PJ49e1bmNv7uCAQCPr5nzZoFRUVFODg44PDhw/w5hWm4uL+Dg4OhrKyM48ePl73BDMa/wBLmvwnionLq1ClERETg0KFDyMzMBCAcKCkoKGDTpk14/vw54uPj0b59e4SFheHgwYPgOI4lA4rBjzriHz9+xOzZs2FoaAh/f3+JY0lJSTh48CBLJJaAS5cugeM4TJ48WaK9S5cumDp1KgBhDTQrKytYWlpCX18fDRs2RIMGDfgELksmloyAgAA0bdoU2dnZEoP91q1bw9bWlv/31atXWe2+EnL9+nX8888/ACBRS/X69eswMDCAi4sLAGDmzJmwsbEBx3FwcHBAy5Yt2QeKYvJvSZQDBw5AV1cXo0aN4me5AED79u3Rpk0btt9ECTl37hyWLVuGoUOHIi0tDYBwdcSmTZtgYGCAbt268efm5eUhOTkZTk5OCAgIKC+Tf3vyx+ry5ctRrVo13Lp1q1jJ8rVr14LjOIm/B8a/s3v3bujq6vIzDc+fPw+O43Do0KEC54r7OiQkBBoaGti1a5fUbP2dEU+47N+/H1WqVIGOjg5WrlyJDx8+/Ot1IkJDQyEvL48DBw6Uub0VheXLl0NNTQ0XLlxAVFQU9u/fDxMTE1hZWRVImuePb3V1dYSHh5eL3b8r4n27sLAw2Nra/mvSPL/POY5jM/p/gvj7Mi0tDX379kX37t0hKyuLiIgIAEK/5h9Hij8bkX7nn5zIKIi4v0VjnePHj8PDwwPNmzfnff5v14n0ZO/evWVuL4PxM1jC/Ddj0qRJqFWrFmrVqoU6deqgTp06/EBn1qxZUFZW5o+bmZkhMzMTd+7cQb169QrdOI5REHHB3rp1K2bNmoXRo0fjxo0bAIS7k8+ePRvGxsYFkuYiWHKreKSlpcHZ2RnOzs58YrFbt26oX7++RNx++fIF//zzDwICArB582a2kVMpEHUMp0+fjvr16/PtoqXiN2/ehKamZoHliiypWDzOnDkDjuPAcRwaN26Mfv364a+//uI3wbl+/Tqsra3h5OQEQLhR1rlz5/DmzRv2gaKYiMfm/v37ERISguDgYCQkJPDxLkp2jRo1CtHR0Wjfvj3q16/Pf5hg8V08Nm/eDBMTE/j6+mL9+vUSxxISErBp0yZoa2tj0KBBfHvbtm1hY2PD4rqE5C/nkZ2dje7du2PBggUAvvc/fjZ7S5QA+NHglVE4gYGB/KrNHTt2QE1NDWvXrgUApKam4suXLwAKJlvU1NTY4L+I5C8NkpWVhbS0NMyePRv6+vpYsmSJxKoVEeIbTYp8vm/fPqnYXBHIy8tD3759MWrUKIm2yMhIGBgYoHHjxkhJSQEguQkfi+/i8W/9jJCQkB/ONM+v31WrVmU+/wnivj506BAiIyORnZ2NvLw8TJgwQSJpLiL/B2QW30VH3N9LlizBgAEDEB8fDwCIjo5G69at0bx5cwldXrNmDV8CFBB+6GT+Zvw/wRLmvxEbN26EhoYGbty4gdjYWERFRaFt27bQ1dXll3deu3YNf//9N/bv38931idOnAhra2u+E88oGhMnTkT16tXRsmVLNGrUCIqKili8eDHS0tIQFxeHOXPmwNzcHOPGjStvU39rREmT1NRUuLu7o3HjxnB0dIS5ubnELKIfdTDZx4mi8SP/PXv2DFpaWhg7dqxE+8WLF2FkZITnz59LwbqKy/Pnz+Ho6Ah7e3u0adMGY8aMgbq6OgwNDdGhQwcEBgZi8+bN0NXVRYsWLQrMcGEJ3KIh7repU6dCW1sb7u7u0NDQQJs2bXDixAmJpHnt2rWhoqICc3NzPsnCErjFY9u2bVBSUsLOnTv5AREgLC8UHR0NQLjyatOmTdDR0cHQoUPh7e0NIyMjtnKihAwYMADdu3cH8D3m09PTYWxsjPHjx/PniR8T9Q9ZArd0iLR46NCh8PHxwZUrV6CqqsonywFg1apV8Pf3l9CS0NBQVKlShfm6iIi/8+bOnQt3d3eJfZhmzJgBfX19/Pnnn/j06RMA4T4Jjx8/5s8JCQlhPi8hXl5ecHZ2LtA+Z84cfvWbuJYEBwdDVVWVfZgoIuLxvWfPHsyZMwfLly/HtWvX+PaQkBDY2tpi6NCh/Ls0/+xbpt8/J3+/UF9fH9u2beP7K8nJyZg4cSLk5eWxfft2JCYmolOnThg4cCB/bXBwMPswUQImT54MXV1dBAcHS4wjo6Ki0KZNGzRt2hT+/v7w9PSElpYWrylhYWFQUlJiesL4v4IlzH8jfH190bNnT4m2pKQkuLi4wNnZucBg/9GjR+jXrx80NDSKtKkF4zv//PMPtLW1cefOHb6TsmjRIlStWpXffOLdu3eYOHEievbsycqBlIDCfJaamoq2bdtCXl4eoaGh/3ouo+iId7SPHj2K4OBgHDx4kF9SHhQUBENDQwwePBhv377F/fv30b59e7i5ubGE7S/g6dOn8Pb2Rrt27XD37l3Ex8fj1KlT6NixI1xdXVGpUiXo6+uD47gCHy4YP0c8RlesWAE9PT3cvHkTgLBGP8dxcHNzw9GjR3kt2bVrFzw8PNgqlRLy+PFjWFhYYMWKFRLtnTt3Bsdx0NbWlkiab968GZUqVYKxsTH7QFFCBAIBPzsO+D6bNj09Hf369UPHjh35lSsibt++jY4dO0qs1FqzZg1LABSBH737rly5gjp16oDjOIlVFWlpafwmzSL27t0LjuOwf//+Mre3ojFlyhRoamri8OHDBWq+T58+HXXq1IGPjw9cXV1RvXp1/u9h48aNUFFRYfH9E34U3/v27YOFhQXWrVsn0b5z504MGDAA1tbW8PLyAiBciVi7dm3m6xIwZcoUaGtrw9vbG1ZWVmjWrJmEnoSEhMDe3h4+Pj4S+h0UFAQNDQ3m82KwcOFC6Ojo4NKlSxKrUERMnToVHMehQYMGMDU15c85ffo0OI5jvi4mERERqFGjxv/Yu/O4mrb3D+Cf06BRA5KhUqkoRTTQVSmkDInMF9c8z4SMmbsyRyhRGTNVrjkkMhOZyTwPSQoN6vT8/uh39rejUHFv4nm/Xvd1taezzmq199rPXvtZdPbsWWFZZmYmPX36lIiIHjx4QP369SNHR0dq3bq1UN+PHz8mDw8Pvl6ynw4HzMuQwYMHk5mZmfCz5GlcWFgY1apVS2o0bnp6OsXGxlKPHj14RudvmDZtWoHXr8LDw8nc3JxSUlKkbuqnTZtGGhoaQl0nJycLARgO6hZd/o76s2fP6OPHj8KyDx8+ULNmzahhw4YUGRnJEx7+QF5eXqStrU2WlpZC2iZJPtXQ0FDS09MjDQ0NMjIyoj/++IPTVPxAt2/fJldXV3JxcaFTp04Jy3Nycuiff/6hZcuWUdeuXQvtzLPCTZw4UXgYLBaL6e3btzRmzBjhRn/Hjh2koaFBvr6+VLt2bbKysqJ9+/YVaM8cuC06yXl4z549ZGJiIgTFiYj+/vtvMjU1pVOnTpG7uztpa2sLr5S/ffuW9u7dK/RbuM6L5/PrX1BQEBkYGAjpEaKiokhRUZHGjRsn/E6SkpKobdu21Lx5c6HNnzx5kuTk5DjP8DfkP0dERUWRn58frVy5kk6ePElERCNGjKBatWrR7Nmz6e3bt3TmzBlq2bKl1CTNRHl98SNHjvzn5S/rYmNjqWbNmsLkkVlZWfTq1SuKiooS/haWLl1KgwcPpr59+wp1npGRQaNHj6Zdu3aVWtnLgvzte9++fbR+/XohD3ZSUhJ1796dmjZtSv7+/iQWi+nVq1fk7u5OPj4+tHbtWjI0NKR79+4REdHVq1dL5TuUZcuXLyd9fX0h1WdQUBDJy8tTgwYNhEFZRESLFi2iPn36CL+v8+fPk5KSEufRLoZ3796Rk5MTLV++nIjyBrvFxMRQv379aNq0afTu3TsiypuLYtu2bUIfJScnhz5+/CgMvmBF5+vrS66urkREdPnyZVq4cCHVrl2bNDQ0aM6cOUSUd6+flpYmnM8l9Z6UlFQ6hWbsKzhg/hP6UuqUQ4cOUZ06dWjJkiVSHfL9+/eTqalpgREYOTk5UjmhWEFXr16VGmUoERYWRqqqqpSSkkJEJNTjs2fPqEqVKhQdHS21PQdzS2bq1KnUoEEDqlmzJq1YsYISExOJKG/COGdnZ7K1taWoqCgOrvwAW7duJS0tLTpx4gSJxWK6fPkyjRs3jqpVqyaMnvj06RMdO3ZM6s0KrvsfJzExkVxdXcnV1ZWOHTv2xe04aP5tly5dIltbW7KzsxMeeGZlZVFcXBwlJSXRtWvXyNjYmJYuXUpEebkr5eXlycrKSgh6seKTBGj9/PxIT09Pqo9x7tw5of+SlJREzs7OpKmpSW/fvpU6BqdhKb7P6ywuLo4sLS3J2tpamIhv48aNVL16dWrQoAHVrVuXrK2tqV69elIPPp8/f85vHBbD+PHjSVdXl1q3bk3t2rUjDQ0N2rVrFz1//pwmTZpEOjo6VL58eapbty41b95cKtUQXztLbu/evaSjo0Pp6el048YNmjx5MtWsWZPKly9P1tbWwnb565gf8BffxIkTSUVFhUxMTEgkEtHMmTOFkaD9+/enmjVrkqamJhkbGwsDtmJjY0lfX1/or7Nvy98mMzMzydvbW5j0OjIykjQ0NMjHx4fatGlDRkZGUqP78w/KevPmDU/QXEwpKSnk6OhIEyZMoA0bNlDHjh2pSZMm1LhxY7K0tKQBAwYUOFdzH6XoCot/REREkEgkol69epGxsTF17dqVVqxYQQsWLCCRSFQg1adYLOY4CvupccD8J3P8+HFycnKSCqZITiLv3r2jAQMGkJOTE82cOZPevXtH9+/fp5YtW1LLli35ZFNMn9fXzp07hZGfGRkZZGNjQ82bN6cPHz4I29y9e5dq1qxJJ06c+E/L+ivavHkz6ejo0MaNG6lfv35kZmZGw4YNo2vXrhFRXtC8efPmpK+vT8ePHy/l0pYt+du2pKM+Y8YMcnFxkdru/v371L9/f3JzcysQ1CLiTuO/ITExkdzc3MjNzY3PI9/p4MGD1LJlS7K1tRVGuUkmIlu7di3Z29vT69eviShvcr5u3bpR//79uV2XUEREBE2ZMoWI8h4qi0SirwZfly1bRm5ublLXUFZ8R48eFXI49+3bV5g3JTY2lqytrcnS0lIImp8+fZo2btxIEydOpODgYE459B22bdtG1apVo9OnTxMR0Zo1a0hWVpZCQ0OJKC/w9fbtWzp06BDdvn2bHzKXUGEB7sTERLK0tKTatWtT5cqVqX///rRu3Tp68OABlStXjt+QKKH8dR0fH092dnZ0+vRpysjIoMDAQFJVVaUJEyZQeno6paen0/379ykgIEDqbc9Ro0aRg4NDoX1G9nXr16+ny5cv0+PHj+np06eUmJhIJiYmtHjxYiLK69OoqamRoaEhhYeHC/vx/X3RfOlh2Zw5c6h+/fqkrKxMU6ZMEe4pBwwYQAMHDvwvi/hLyV/fKSkplJWVJfTBAwMDycXFhdasWUMPHjwgorxULI0aNeKHbazM4YD5T+bWrVvUpEkTat26tVQwRXKD/+rVKxoxYgTVqVOH5OXlydzcnBo0aMAjK0pAUlc5OTn0+PFjUldXpw4dOgivX+3Zs4dsbW2pYcOGdPz4cTpw4AC1adOmwIQ3rGg+b5sbN26Uyn+7cuVKatCgAQ0ZMkQYQZGWlkYjRozg+i6mws4Dy5Ytozp16tDz58+lloeHh5OKigrdv3//vyreby8xMZFat25N1tbWdPny5dIuTpmTfwT+1q1bycXFhf744w+6ffs2EeXdXM6bN48sLCzoypUrlJqaSu7u7lLnGz6nFF/fvn3pjz/+ICKiR48ekZmZGTVs2FDIryq5USLKCya2bt2aRo4cWSpl/RXk5ubShw8fyNzcnJycnKhTp06koaFBly5dIqK88/zRo0eFoLlk9P/nuK0XzefXTV9fX+rZsycR5Q2oKF++vDC3SlpaWqGpKLgPXjz56+v8+fN07Ngx4QHFlStXaPbs2bRr1y7hbc+kpCSytbWlo0ePlkJpy67PH2zOnz+fhgwZQoMHD5ZaHhQURKqqquTt7V2gr3jhwgUaPXo0qamp8VsqRZS/fc+fP5/U1dXp+vXrwsOHTZs2UYMGDYRJKPfs2UNt27alxYsX87mkmPLX144dO2jFihU0Z84coR2/ePGC7ty5I7VP8+bNhQfQrHjyP8SZN28eNW/enCwsLKhHjx4UHx9PRP97O18sFlNGRga1bNmSnJ2duW2zMocD5j8hyQhEV1dXqaC5JEiQlZVF79+/p7///psuXbrEOUFLIP/JWnKTf/z4cTIxMaFOnToJN0KxsbHk4uJC6urqZGZmVuCVW1Y0+S+s69atIx8fH+rWrRutXLlSajtJ0HzYsGEFOuRc30Wze/du6t+/P/Xt21fqtc6DBw+Snp4eLV++XLj5JMq7CapXrx4/8f+P3bhxg8aOHcsdx2L6vJPu6elJdevWJZFIJJWe5c6dO1SlShUyMDAgPT09qlu3Lqe6KSHJuTcoKIhsbGyE5QsWLKDq1atT06ZNpSYlk+Trr1evHs9B8QN8/PiRqlevTrKyshQUFCS1TiwWU2xsLNna2kqlZ2Elt2fPHnr27BnNnTuXxowZQ1FRUaSqqkqrVq0iory2vG3bNpoxYwbX93fIf07w9vamOnXqkL6+PllZWVHr1q2lts3KyqIXL16Qu7s7D1oppj///FNqIlqivAknRSIRWVtbF8gZvGbNGtLQ0KBhw4ZJrQsLC6P27dvzvFglcPPmTZoxY4YwmWH+ycdNTEwoMjKS3r9/T+7u7jRp0qQCeZ1Z0Y0dO5a0tLTIwcGBtLW1ydjYmFatWkXv378nory39c+dO0ctW7Ykc3Nzjp18pylTplDFihVp1apV5O3tTW3btiVlZWWKjY0lory3xTds2ECOjo5Uv359HuDJyiQOmP+k8gfN4+LihOW5ubn07NkzcnNzk3qNiC+qRZf/JL1ixQqaMmWKMOnHqVOnyNDQkDp27Cg18vPGjRv07NkzfuW2BPLXt7e3N5UvX54aNmxICgoKwgjQ/FavXk06OjpCfj8OtBRdYGAgqampUb9+/ahJkyZkZWVFe/bsEdZPnDiRKlSoQHPmzKHjx4/TvXv3qEWLFuTo6Midl1LEdV98y5YtI1VVVTp06BDdvXuXVq5cSY6OjtSwYUPhnHL37l0KCgri1BQ/yNmzZ0lNTU2YiI+IaNasWVSjRg1SUFCgzp07U5MmTahRo0ZkZ2fHD5e/Q/434J4+fUpWVlZkampKLi4udPDgwQLbxsbGko6ODvXp06c0ilumSUbsE+VN7F69enW6d+8ehYSEkLKyMpUrV04IlhPljS5v0aIFjR07thRK++tZtGgRVaxYkU6fPk3Z2dk0e/ZsEolEwijyrKwsCgkJEea14fNK8dy/f18YGJT/webChQtJJBLRokWLhGCixNKlS6l58+YF+t9feouFfVlsbCyJRCJSVlYW5guSuHXrFrm5uZGOjk6BB/t871N8O3fupKpVq1JCQoLQ5vv160fW1ta0fv16IsobVOTk5ERt2rThc8l3evLkCVlaWtLOnTuFZZL5DypWrEg3b96kt2/f0po1a2js2LHcD2dlFgfMf2KFjTR/+fIlOTo6Us2aNXm0XAnk74B4eXlRtWrVaPXq1cJs70R5I80NDQ2pU6dOdPbs2QLH4OBWydy4cYOGDh0qBFu2bNlCTZo0IU9PzwKvNkdGRnIHppiCg4NJVlZWGMHy9OlTMjU1paioKMrMzBS2mz17NllZWZGioiJZWFhQw4YN+Yk/KzNyc3MpOzubunXrRkOHDpVaFxUVRRYWFtS4cWO6efNmgX35nFI8+/fvp7lz59L58+fp/v37dOfOHTI0NBTm+pA4ceIETZw4kVq1akW9e/emoKAgfvPtO+Q/Dx86dEgYxZycnExWVlbk7OxM0dHRBQIqN2/e5DZeTCtXriQzMzN69+4dPX/+nIYOHUr79+8X1o8ZM4ZEIhFt2bKFLl26RFeuXKEWLVpQ/fr1+e2JEvq8vnr27EnBwcFERLRr1y5SU1MT3qRIT08nory/A39/fw64FFP++8TVq1eTnZ0dxcTECMtmzJhBMjIytGzZsgJB8/yTTXIbL7rC+tHz588nkUhE06dPL9B2b9++Tfv27aMNGzbwdfM7rVixgqysrOjDhw9S5+eOHTtS/fr1he3Onz/PA+B+gNu3b5OCgoLUNZMoL37VqFEj4S3y/PPYcB+FlUUcMP/JSYLmLVu2pH/++YdcXFzI1NRU6ATxib5oJHm0JNatW0dVqlShc+fOCctyc3OlJs4yNjYmFxeXQgMvrHh27NhBurq6ZGVlRa9evRKWb9q0iZydnal9+/bCZJ/58YW1aMLDw0kkElFYWJjUcisrK3JwcCALCwtq06aNkMvv3r17dPbsWTpz5gx3GtlPT9JG89+09+vXj5o1a1ag3Y4bN45EIhHVrFmT7t69+5+W81eSmppKrq6uZGZmRkZGRqSiokJubm4kEonI3d2dTp8+TRcvXvzqMfj8XXyfp6kwNTUlf39/oW/y5MkTatCgAbm4uNCePXvo06dP1LhxY5o6daqwH9d70axatYpEIhFFRETQ3r17SSQSkYGBAZ05c0Zqu8GDB5Ouri6pqqqSra0tOTs788jEEsofTLxy5Qp9+vSJbG1tKSQkhA4cOECqqqpCkCUnJ4f8/PwoKipK6hhc58WXkpJCd+7cIRMTE2rbtq1UDngfHx+SlZWl5cuXFxhBzoHyktu4caPUG+IzZswgWVlZWrdu3Vf34/ZdfJLzyvz588nY2FhYLnngdufOHVJSUpJKc5t/P/Zt+c8Fkn9nZGSQk5MTjRkzpkB6skaNGnF+ePbL4IB5GZCYmEitWrUikUjEwfIS6Nq1q9DhlpzkR40aRX/99RcREV2/fp1Wr15NDRo0oJo1a9L27duJiOjIkSPUqVMnvqD+AFFRUdSyZUtSVVUtMJp88+bN1Lx5c3J0dOSJJ0toz549JBKJaNKkScINj6enp5CzfP78+aSjo0P29vaF7s9tnP2sNm/eTH/99Rddv35d6mY+ICCATExMaO/evVJvUKxdu5ZatmxJs2bN4hvP7yTpYzx+/Jiio6MpPDycjIyMhAcSampqZGtrSy4uLjRq1CiewPYHmj59OlWsWJFOnDghjPyU9F8eP35Mf/zxB9WpU4dMTEzIwsJCasJV9m1bt24lkUgkBFDS0tLozz//JJFIROHh4QW2v3LlCp04cYKuXbvGD5lLKH/AZcKECeTi4kL379+nESNGUNOmTUldXV0q9c2LFy+odevWtGLFitIobpkWERFBkZGRRJSX07l///5ElPemp5mZGbVu3VoqaD5z5kwSiUTC/Q/7Pu/fv6dKlSqRvb291AO4adOmkaysLIWEhJRe4X4BX7pnefr0Kamrq9OgQYOklp87d45q1arFA+BKKH99Z2ZmSgXHZ86cSRYWFhQYGCgMTvz48SP98ccf5Ofn95+XlbF/AwfMy4ibN2/SiBEj+HXEEpg0aZJwMyn5/8KFC6ly5co0btw4ql+/Pnl6epKPjw8NGjSIypcvT69fv5Y6BgcUi+5LdXXkyBFycHCgBg0aFMhbHhwcTCNGjOB6Lqbs7GzhJjQyMpJEIhF5e3uTh4cHmZub04MHD4Rtw8LCpAIEjP3s3r17RzVr1iQtLS0yNzenXr160dq1a4X17du3J2NjY9qyZQs9efKE3r17Rx4eHuTj48OTZv0AhY0uHDlyJI0dO5ZevnxJly5dosDAQOrZsyf16NGD67qENm7cSG/fvhV+vn//PtnY2NCBAweIKC9wePbsWRo7dixt27ZNWBYWFkarVq3ifmExrVmzhkQiEcnIyEi9JfH+/Xvy8PAgbW1t4e3DL42w5b5KyV2/fp3++OMPoS9y+vRpYfT+7du3iYjo+fPn1KpVK7Kzs+PzSjG9f/+eBg4cSPLy8tS+fXtSVlaWytP/paD52rVr+RxSQoWdJyRpEZ2cnOj06dPC8mnTppGCggI/CCqh/OfeqKgoWrhwIYWFhQl1vHnzZipfvjz16NGD4uPj6cKFC9SmTRuyt7fn83YJ5K8zX19fcnFxISMjI+rbt69w/Rw2bBjVqVOHmjdvTl5eXmRvb0916tTh8wn7ZXDAvAziE1DRfH5hXLlyJQUEBFBGRgbdv3+fpk6dSnXr1qWlS5fSjRs3iChvchZHR0eptCGs6PLXeUxMDO3bt4927dolLDt69Ci1bNmSbG1tC4w0L+wY7Mvyj7ZNSEggorzUNyKRiBQVFSk+Pp6I/teR37NnD9WuXZsSExP/+8IyVgI5OTk0adIkWr16NcXHx9OCBQtIXV2dOnToQMuXL6fs7Gxq164d2dnZkbq6OpmamlKtWrU4t/C/aPXq1VSjRg1KTk4udD0Ht4onMDCQXF1dpa57b968IX19fZo/fz6dP3+eevToQfXq1SNbW1sSiUS0cePGAsfhei+aoKAgkpOTI39/fxo4cCBVqFCBYmNjhfUfP36kVq1aUbVq1aQmt2U/xrx588jDw4M6dOhAHz9+FJYfPnyYKleuTFZWVmRiYkJ2dnZkbW3NqW9K6M2bN1SrVi0SiUS0dOlSIsq7d5RcG2/cuEHm5ubk7u4uPJiT4HvMknv37h0R/a/v8ezZMzIxMSEHBwepkeajR48mBwcH7qN8By8vL6pSpQrZ2tpSnTp1qGLFisLEnnv27CEDAwOqWrUqGRkZkYODA8/V9J2mTp1KFStWJF9fX1q6dCmZmpqSvb29kL88NDSU+vXrR61ataLhw4fzuZv9Ujhgzn5Zko6I5OLYunVrMjQ0pLCwMGGkef5JbrKzs8nNzY3atGnDnZjvJJlQtWbNmqSsrExNmzYVRmwdPnxYGDn0rRy4rHCSdEEZGRk0YsQIql27tjBCUZKL1cvLi16+fCns06ZNG2rTpg13FlmZsn//flJTUxPSfWRkZND06dNJJBKRk5MTzZs3j1asWEFbt26ljRs3Cp1z7qT/eLm5uRQbG0vVqlUTzjeSeuaJ4UpOUoenTp0S5pmYPHkyGRgYULly5Wj06NG0d+9eIiJq27YtjRw5stTKWpZt376dRCIR/fPPP0SU9+Zmz549Cw2at27dmnR1denkyZOlVdxfUmhoKIlEIqpSpQrdunWLiP7XV7927RqFh4fT3LlzKSIigidALKb8fbvXr19T9+7dqUOHDqSurk47d+4UtpHc/9y6dYsqV65MY8eOLZXy/moWLlxITZo0oXv37kktf/78Oenq6pKjo6PUhNn5J1VlxRMREUGVKlWiU6dOUW5uLt2/f598fHxIRkaGNm/eTER5+csvXrxIV69e5TRa3+nevXtUu3Zt4dpJ9L+3gOzt7enFixfC8vx1zPXNfhUcMGe/pPwdx/wTv/31119kYmJC69atE4Ll79+/p507d5KzszPVq1ePn0J/pzVr1pCWlhbFx8fT06dP6d69e2RhYUF2dnbC67b79++nRo0aCXkVWfGsWrWKGjduTObm5lShQgWhjUtuMHfu3EkikYjGjRtHr169otatW5OJiQm3bVYmDRs2jIYOHSr8bGZmRu3ataNx48aRu7s7iUQi4SaJiIPl/6bMzEyqUaMG7du3r7SLUublf9gQExNDysrK5OvrSx8+fKD09HS6efOmVCoFsVhMjRs35rygJfT+/XuKiYmRWnbr1q0vBs0bNmxIbdu2/a+L+cv4Uj8jIiKCRCIRDR8+nJKSkr56DD6XF03+uj5w4ABdv36dMjMz6dWrVzR06FBSU1MTguYS6enp9PLlS67jH+TKlSukpKRE7du3F4Lmkt/L9u3bSUZGpsDbtRws/7b8dSSpT8nDifxSUlJo7NixVL9+fXr8+HGB4/B9T8k9evSI9PT0hNHkknvJV69ekaamJi1evLjAPty22a9EBoz9YnJzcyEjk9e0Z82ahW7duiE6OhoAEBYWBhsbG8yfPx/bt29Heno6kpOTcfXqVRgbG+PChQuQl5dHTk6OcAz2ZQcPHkRKSgoAgIgAADdv3kSTJk3QoEEDVKlSBYaGhjh27BhevnwJHx8fAICbmxuWLVuGwMDAUit7WTZ48GDo6Ojg+vXraNSoETQ1NYV1ubm58PT0xM6dO7Fs2TLo6enh8ePHuHbtGrdtVibVr18fly9fxtu3b9GgQQNoamoiLCwMCxcuRFBQEMLDw9GpUydhe1lZ2VIs7a/v48ePePToUWkXo0wjIqGdikQiODs7Y/jw4QgMDERAQADS0tJQu3ZtWFpa4uPHj7h06RLc3d3x4cMHjBkzppRLX7YQEcRiMVRVVeHs7AwAEIvFAIBatWph6tSpaNOmDTw9PREbGwsAUFZWxrFjxxAZGVlaxS7T8vfDb9y4gTNnzuDZs2fIyspC+/btERYWhoCAACxYsADJycnCfpJ+pASfy7+NiIS6njx5MgYMGICEhARkZ2ejcuXK8PLyQo8ePdCvXz9s374dANCuXTtMnToV2trakJWVFf4eWNHk5uZK/SwWi2FhYYFz587hyJEjGDNmDO7fvy/8XogIvXv3hqGhIUxNTYX9RCLRf1rusih/XUvqU1NTE4mJiXj8+LGwTkNDA02bNsWjR4/w8ePHAsfh+56iefToERISEpCWliYsU1JSglgsxpkzZwDknZdzcnJQuXJl2NjY4PXr1wWOw22b/VJKM1rP2L9p8uTJVLlyZYqKipIaZU5E1KNHDzI1NaWwsDD69OkTffjwgSeJK6bAwEBSUVGhVatWSeXt69atGzVv3lzYTjJr9o4dO6hatWr08OFDqePwU/+ikbTPT58+UUZGBi1dupRmzJhBzs7O1K1bN6FeJa/bEuVNBGpnZyeMBuDX41hZZWNjQyKRiJo0afLF/Nncvv8bGzdu5Lr+DvlHXu3YsYPCw8OFn729vUlXV5fmz58vzKWyZcsW8vDwoKZNm3Je0GLK379ITU2V+llSl0REt2/fpl69epGWllaBnM7cRyme/O174sSJZGJiQioqKlSvXj3q1KkTpaamEhHR+vXrhYnKX79+XVrF/WXMmjWLtLW16cSJE1L54YnyUrSMGDGCRCIRWVhYkLGxsVT7Z0WX/3wQHh5Os2fPpsmTJ9PZs2eJKG9SWzU1NWrXrh0dPHiQXrx4QW3btqWAgABhPz5/F82uXbuoZ8+e1LlzZ1q0aJGw/OzZs2RpaUk+Pj707NkzYfn169fJzMxM6s0sVnRhYWFkZmZG2traVLNmTTpy5IiwLjg4mOTk5Cg4OFhYlp2dTZaWlvzWG/vlccCc/ZJu3rxJZmZmtGfPHqnl+TuIPXv2JHV1dSE3KBG/QlRcQ4YMISMjI1q5cqUQxNq/fz8pKSnRmjVrpLbdunUr1a1bV8h9y4oufwc9PT1dat2KFSvIwcGBunXrRo8ePRKWx8XFSW3HAS5WFknOyRs2bCBzc3O6cOGC1HJWevicUnz5z+UJCQlkampKLi4uUkHaSZMmkZ6eHs2fP5/S0tIoOTmZYmJiOKfzd5g3bx41bNiQ2rRpQwsXLhSWfx40d3d3p5YtW5ZGEX8Zkja+ZMkSqlChAkVHR9OVK1doxYoV1KhRI3J0dBQmLd+8eTOJRCKpYCIrvuTkZHJwcKCgoCAiypts8sSJEzRkyBBasWKF0O8+fPgwrVmzhs8lP4CXlxfVqFGD2rVrR927d5eakPnmzZtUp04d0tHRoerVq1ODBg34AUUxBQYGkpqaGg0aNIg8PT3J0NCQNm3aJKyfMWMG1a5dm0aOHElHjhyhq1evUosWLahx48b8kLMEVq9eTQoKCrR69Wq6evUqNWnShGxtbYX1Hz58oKlTp5JIJKJu3brR8OHDqWnTpmRmZsbnEfbL44A5+yUdO3aMNDQ06M6dO0QkHVzJH3CcMWMGP+kvgczMTOHfAwcOpDp16lBAQAC9e/eO0tPTycvLiwwMDGjFihX0/v17evr0KbVu3ZpatWrFga7v4OvrS05OTtS2bVtauXKlsDwgIICaNGlC7du3p5MnTwqdRq5r9qt4+vQpVa1alXx9fUu7KIx9t0mTJlGfPn3I3NycFBQUyMHBQWpCrcmTJ5O+vj5NnTpVCC4S8WjnospfTwEBAVShQgWaN28ede7cmczNzWnAgAHC+vyBrMePH3Mdl5DkYSZRXh+xU6dONGPGDGFZdnY27du3j6ysrGjKlClCPR86dIgDLt8hNzeXXr58SWZmZjR//nzauXMndevWjRo3bkz16tUjS0tLmj59eoF7Hb73KT5JO42IiKBq1arRuXPniIhoz549JBKJpAK6z58/p0OHDlFUVBQ/oCgmyWjmiIgIIsrLld24cWPaunWrMP8YEdHSpUupadOmJBKJqG7dulJv1PJ5vOjWrl1L5cqVkxpAGB0dTe3bt6fdu3fT0aNHhfkmdu3aRa1atSJPT08aPHiw0Kb5fMJ+ZRwwZ2Ve/qCg5N+XL18mfX192rVrl7BOcjIPCwujHTt2SB2DT/RFl7++Q0ND6e+//yYFBQXS1tamVatWUVZWFj179oymTZtGSkpKVL16dTI2NiYrKyvuyBRT/nry8/OjihUr0sSJE6lLly6kpqZGkydPFtYHBweTs7MzVa9enRwcHKRSszD2K/D396eKFSvS9evXS7sojJVYQEAAqamp0enTp+np06cUFxdHDRo0IDc3N6kb1uHDh1P79u35wed3iImJofnz59Pu3buJKC8ty+rVq8nIyIj69esnbPf59ZL7KMWzevVqEolEdOvWLWGZi4sLdenSpcC2/fv3p+bNmxeoYw4mFs2X2uaUKVOoevXqpKKiQt7e3sIEtx07dqRhw4b9l0X85URHR0udh5cvX059+vQhorwJPVVVVSkwMJCIiN69e1cg9SQR32cWVVRUFIlEIvL395daXr9+fbK2tqYaNWqQi4sLxcfHE1FefV+6dImuXbsm/G3wuaToHjx4QNWqVaM//vhDarmzszNVqVKFdHR0qGrVqmRjY0P3798nIirwtgTXN/vVccCclWmfdxwlo8dTU1PJ2tqamjdvLjUjeXZ2Nrm5udHAgQP/03L+inx8fEhDQ4M2bdpEYWFh5OHhQVpaWrRq1SphBPrdu3dp+/btdPDgQR5h8R0uXLhAAQEBdPDgQSLK6yAGBASQrKysVND88ePHdPnyZe40sl/S3bt36a+//uJgFivT+vXrR56enlLLTp06RQYGBtS4cWOpoLnkuslB8+KLi4sjHR0dqlSpklSKstTUVAoMDCQTExOpkeasZFavXk1ycnIUGRkpLMvNzSUfHx9q2LAhnT9/XuqcvWLFCrK3t5caKcqKJn89RkVF0dq1a2nJkiVCXvjbt29LPbQgyntw4e3t/Z+W81eSnJxM+vr6VLt2beE87OPjQx4eHrR9+3YqX7681Buf69evp8GDB0u9GcSKLioqitTV1WnMmDH04MEDIiLy9PQkAwMDCgoKorVr15KRkRHZ2NgUmuaG+4fFk5KSQkFBQVS9enXhwVrnzp2pdu3adOnSJUpLS6N169ZRtWrVyMfHh8RisdS9JfdN2O+AA+aszMp/UVy4cCF169aNatWqRX5+fvT48WN69OgR6ejokLOzM02ePJkCAwOpSZMmZGFhwYHE75Cbm0tv3rwR0rDk17t3b1JXV6dVq1bRmzdvCuzLIyyK79ixYyQSiahixYpSN/0fPnyggIAAkpeXp6lTpxbYj+ua/Yp4cmZWVkn6LMOGDSM3NzciymvPkra8du1aUlZWpnbt2tGhQ4eE/fiGtGQeP35M06dPpwoVKtDIkSOl1qWmplJQUBCpqalxmqfvsG7dOpKVlZV6yEOUN8lkUlISGRsbk5ubGx09epQyMjIoNTWVmjZtSt27dy+lEv8axo4dS9ra2mRpaUlVq1YlPT09ioiIoIyMDCLKG1Rx/vx5at26NZmbm/M9z3fIzc2lkydPkrm5OVlaWlJubi6dO3eOTE1NSVFRkRYvXixs+/79e2rTpg0NHz6cz9vfYfv27aSjo0OjRo2iFi1aUN26daVG7e/du5dEIpHUpJSs+CR9j9TUVAoJCaHKlSuTtrY21a9fn16+fClsJxaLycLCgt9UYb8tGTBWRsnI5DXfyZMnY8GCBbCxscHYsWMxb948jBw5Enp6eoiNjYWBgQH27t2L9evXQ0dHB/Hx8ZCTk4NYLC7lb1A2iUQiyMvLg4ggKysLAMjMzAQAhISEoFatWli2bBnWrVuHDx8+SO0r2Z4Vnb6+Pnx8fJCRkYEzZ84Iy1VUVNCrVy/4+/tj7ty5CA4OltqP65r9ikQiEQBu3+znl5ubK/WzpM/i5OSEgwcPYtu2bRCJREJbLleuHJydnfH06VNs3rxZ2E/S5tmXfV7XAKCrq4uhQ4di5MiR2Lt3L3x8fIR1ampq6Ny5MzZs2IDx48f/l0X9ZZw8eRL9+vXD2LFj0apVK2F5p06dsGjRIlSqVAmHDx/GixcvMGrUKJiYmKB58+ZISkpCSEgIAICISqv4ZVZ4eDg2bNiA6OhoHDt2DE+fPoWNjQ28vLxw8uRJAMCJEycwdOhQiMViXLx4ke95voNIJEKjRo0QFBSEjIwM2NnZwcbGBh06dED58uWRkZGBa9eu4fTp0+jUqROePn2KJUuWQCQScfsuJkl9dezYEYsWLUJ4eDjOnj2LxYsXo0aNGsJ2CgoKqFWrFrS1tUurqGXasWPHsGjRIgwbNgzp6elQU1NDx44dMX/+fCgrK8PExESo29zcXHz48AFqamrQ19cv3YIzVkpExGdzVobFx8ejR48eWLduHezs7HDhwgU0atQI69atw19//SVsl52djczMTJQvXx4AkJOTAzk5udIqdpmSm5sr3Ojn16JFC3z8+FHooGdnZ0NeXh5du3bF8ePH4ezsjI0bN/LNfjF8qa5TUlLg5+eHJUuWYMmSJRgyZIiw7sOHDzh06BDc3d25TTPG2E8g/7n8yJEjSElJgYKCAlq0aAEFBQVMnDgRS5cuRWBgIBwcHKCpqYnevXvD3d0d2traaNeuHa5evYo6deqU8jf5+eWv6w0bNuDevXt4+/YtevbsCRsbG6SkpMDf3x9btmxBly5dMHPmzALHEIvF/BCumNLT0+Hq6gogb+BKy5Yt0bVrV1y8eBGHDh0SAlzJyck4d+4crl+/Di0tLXTv3h1ycnLcDy+hhQsXYs+ePTh06BBEIpFQh25ubkhKSkJ8fDwA4MyZM7C1tYWMjAzXdTGdO3cOycnJaNmypVB3OTk5uHjxIrp27QodHR0cP34c06dPx549e5CQkABbW1uoqalh7969kJeX53NKEX3pvgcAdu3ahWHDhqF9+/YYMmQIzMzMAABt27ZFdnY29u7d+8V9WeHCwsLw999/o3379qhZsyb69esnrEtJScGuXbvg7e0Nd3d3rFmzBgDQqlUrvHz5EufOnePzCPs9lebwdsa+15kzZ6hBgwZERLR161ZSVVUVcsm9f/+eDh06RB8+fJDah1+TK7r8aW8uXbpEd+7coefPnxMR0bVr16hKlSrUvn17qW27du1KJ06cEH7m+i6a/HUdEBBAI0eOJFdXV9q+fTu9evWKMjIyaOrUqVS+fHlatWpVocfg124ZY+zn4eXlRXp6eqSnp0f6+vqkr68vTFrr4+NDysrKwnozMzPKzMykS5cukZGRUaETx7EvGzduHFWuXJlcXFzI1taWFBQU6O+//6aPHz9SUlISzZw5k+rUqUOjR48u7aKWeZK+xocPH6hp06bUqFEjsrOzozp16tCLFy+E7b6UT5hTahWfpC89efJkMjY2FpZL5m66cOECVapUiRISEqT245zOxRMTE0MikYhEIhE1atSIevfuTZGRkfTo0SMiIjp79ixZWlpS48aNiShvAsTY2Fh69OgRzx9UTPnbZkREBK1evZpWrVpFb9++Fdp7eHg4Va9enYYPH043b94kd3d3MjY2FvKXc/suug0bNpCSkhJt3ryZkpOTheVTp06lmzdvElFeOqeQkBCqUqUKDRo0iNq3b08mJiZCffO5m/2OOGDOyowXL17QlStXaMOGDXT16lV6+/YtXb58mbS0tCgoKIjU1dWlcmofPnyYPD096fbt26VY6l/DhAkTqEaNGqShoUEdOnSg3bt3ExHRnj17qFq1alSrVi1q06YNNWjQgIyNjYULKndkim/ChAmkpaVFs2fPpv79+5OhoSH17duXsrOz6fnz5zRt2jTS0NCgBQsWlHZRGWOMfcG6deuoQoUKdO7cOXr+/Dldv36dWrZsSdWrV6fHjx8TUd5D/927d1NERIRw3Rw3bhxZWloWOg8IK9y+fftIW1ubLl26JPQ7fH19SVNTU+gXPnnyhMaNG0d//vknP8gvocLq7cOHD9SyZUuSl5enwMDAr27Liu5L/ec7d+6QlpYWjRo1Smp5XFwcmZiY0N27d/+D0v267t69S3Z2dmRtbU1ubm40cuRI0tDQoJo1a1Lbtm1p6dKlFBoaStWrV6fmzZsXaOd831M0+ett4sSJpK2tTU2bNqUKFSqQm5sbRUdHSwXNa9SoQaqqqlSnTh0heMsPJoru1q1bZG5uTkuWLJFa3qFDBxKJRKStrS0VNA8NDSVFRUWqVasW1zf77XHAnJUJO3fupFatWlGVKlVITU2NlJSUqG3btnT69GkaPnw4iUQimjFjhrB9ZmYmtWnThjp06MCdlxLI35E5cuQIGRoaUmxsLIWGhlLnzp3J2tqaIiIiiIjo1atXNG7cOBo+fDiNHTtWuKDyU+jiO3r0KBkZGdGFCxeIKK/u5eTkaOPGjcI2ycnJNHLkyEI76owxxn4OkyZNoj///FNq2bt378jBwYHs7e0L3HzeuHGDevfuTRUqVCgwSpT9z7Rp04RR+hLh4eFkbm5OKSkpUvUqecAsGfWcnJwsXDf5+lk8+fvSz549o48fPwrLPnz4QM2aNaOGDRtSZGSk8DvgOi6Z/HW9f/9+WrVqFe3atYuuXLlCREQrVqygmjVr0oABA+jx48d05coVcnd3JycnJ77n+QESExOpffv21Lp1a0pISKDk5GQ6fPgweXh4kKOjIykqKpKuri6JRKICDy7Yt+Vvo0uWLCEdHR3hvmfLli0kEonIycmJ9u/fL5xDtmzZQq1atRLOLRy8LRpJXe/Zs4dMTEyEoDgR0d9//02mpqZ06tQpcnd3J21tbbpx4wYREb19+5b27t0r3MtzfbPfGQfM2U8vKCiINDU1aeHChXT48GFKSUmhWbNmUe3atalWrVo0ffp06tq1K9WoUYPCwsJoyZIl1KJFC6mn0NyBLJmIiAgaPHgw+fr6Csvi4+Ppr7/+IisrKwoPDy90P76wftvMmTML3PT/888/ZGdnR0R5AYDy5ctLpRg6fvw4EfFNP2OM/ewGDx5MZmZmws+SG8+wsDCqVauWVOqK9PR0io2NpR49eghBMVbQ1atXpYImEmFhYaSqqkopKSlERJSRkUFEeYHdKlWqUHR0tNT2fN0sualTp1KDBg2oZs2atGLFCkpMTCSivD6Ks7Mz2draUlRUFPcDfwAvLy/S1tYmS0tLIW3Tli1biIgoNDSU9PT0SENDg4yMjOiPP/7ge54f6Pbt2+Tq6kouLi506tQpYXlOTg79888/tGzZMuratatQ5+zbJk6cKDwMFovF9PbtWxozZgytWbOGiIh27NhBGhoa5OvrS7Vr1yYrKyvat29fgfbM55aiS0tLIyIiPz8/0tPTE66NRETnzp0T3mRLSkoiZ2dn0tTUpLdv30odgwfAsd8dB8zZTy0oKIjKlStHO3fuLLAuPDycrK2tydHRkcLDw2no0KGkq6tLzs7O1K9fP34K/Z3u3btHjo6OpKGhQV5eXlLr4uPjqVevXmRra0tr164tpRKWXYcPH6Zu3boVaJtr166lRo0aUXR0NKmpqdGKFSuEdbt27aKhQ4cKOeSJ+KafMcZK25dSpxw6dIjq1KlDS5YskTrX79+/n0xNTYW0LBI5OTlSN7NM2ufXu507dwqBrIyMDLKxsaHmzZtLzVtz9+5dqlmzJp04ceI/LeuvavPmzaSjo0MbN26kfv36kZmZGQ0bNoyuXbtGRHlB8+bNm5O+vr7wgJ+VzNatW0lLS0uYE+jy5cs0btw4qlatGu3YsYOI8vJnHzt2TCoVEd/z/DiJiYnk6upKrq6udOzYsS9ux0Hzb7t06RLZ2tqSnZ2dMFgoKyuL4uLiKCkpia5du0bGxsa0dOlSIsobQCQvL09WVlZ08uTJ0ix6mRUREUFTpkwhoryHyiKR6Ktvry1btozc3NwKzP3G2O+OpxZmP63Y2FgMGjQIU6ZMgaenJyjvAQ9ycnIAAF26dEHPnj1x+fJlyMvLIyAgAPHx8YiJiUFwcLAwqznP6FwyhoaGmD59OmxsbBAZGYnDhw8L6xo0aIBRo0ZBW1sbJ0+eLMVSlk3NmjVDWFgY5OTksGvXLpw+fRpAXpt++fIlXF1dsWLFCgwbNgwAkJWVhcDAQKSmpqJKlSrCcUQiUamUnzHGGBAXF4e5P+GjAAEAAElEQVSOHTvi+PHjwjIiAgDY2Njgjz/+wK5duzBv3jykpqbiwYMH8Pf3h76+PnR0dKSOJSsrC0VFxf+0/GWJpF7FYjGePHmCvn37YtGiRYiPj4eioiJ8fHyQlpaGZs2aIS4uDgcPHsTo0aNRqVIlNGrUqJRLXzbl5uYW+HncuHHo3r07goODMXz4cJw+fRoBAQG4ceMGVFVVERERAXd3d/zxxx+lVOqyR9K2gf/V+c2bN2FpaYnGjRtDRkYGdevWxbBhw9CqVSsEBwcjJSUF8vLycHR0hKWlJWRkZCAWi/me5wcyNjbG8uXLIRKJ4Ovr+8X7HXl5+f+4ZGWPpaUlZs+eDQ0NDfTp0wfXrl1DuXLlYGtri0qVKuHs2bPQ1tbGn3/+CQB4//49OnbsiPr166Nhw4alXPqyac+ePTh69CgAwMnJCaamphg0aBAePXoEAPj06ZOwbVZWFqKjo2FiYgIVFZVSKS9jPysOmLOfVvXq1WFvb4+LFy8iLi4OIpEIIpEIcnJyQody5MiR0NXVFYK5Ghoawv5ExB3HIsp/U5S/496sWTNMmDABtWrVgp+fH2JiYoR19evXx8KFC7FmzZr/tKxlmbe3N7y9vQHkdbCvXbuGcePGYfny5Th//jxUVFSwePFiVK9eHVu2bEFMTAy2b98ODw8PPH78GKGhoRCJRFK/I8YYY6WjcuXKICL4+fkJwRSRSASxWAx1dXXMmTMHFhYW2LZtG7S0tNC2bVu8evUKu3btgkgkKhCQZIXLzc2FjEzeLYtYLIauri52796Nq1evYv78+bh27Rpat24NPz8/qKmpwd3dHWPHjkVmZibi4uIgKysLsVhcyt+ibCEioc5DQkIwY8YM7N27FwoKCsI2Q4YMQf/+/XH27FmsXLkSly9fRvny5eHv7891Xgz5+3SSOtfU1MTz58/x4sULYZ2BgQGaN2+OuLg4vHv3rsBxZGVl//Wy/m6MjY2F9jx69GhcuXKltItU5mRnZwMAWrRogd69e0NdXR2DBg1CYmIiypUrByLCq1evkJqaipcvXyItLQ3h4eGwtbXFmjVr+FxSTJK6atSokVD3enp66NOnD54+fYq+ffvi0aNHKFeuHAAgMTERHh4eePr0KRYtWgQAfJ/JWD4i4r8I9hO7c+cORo4cCSLC1KlTYW9vDyDvRC4SiZCWlgYrKyv89ddfmDZtWimXtmzKfyO6bt06nDt3DuXKlUOjRo2EJ/379+9HQEAAPn36hEmTJsHZ2fmLx2CFS01NxciRI5GYmIh27dph4sSJAIAtW7Zg2bJlMDIywoQJE1C3bl0cO3YMY8eOxZs3b6CtrQ0DAwNs3LgR8vLyEIvFfFPEGGM/ifz9lGnTpqFx48YA8oIE8vLy+PTpEz59+oSAgAC4urrCwsICsrKy/AZcEeXvXwQEBODFixcYP3481NXVcfr0afTo0QMNGjTAtGnTULduXQB5o3PV1dVRpUoVyMjIcF0XU/46nzRpEgICAmBmZoaEhASYmJhg06ZNsLCwELYPDAzEnDlzMGrUKHh5eQl9dPZte/bswa5du5Cbmws7Ozv0798fABAdHY0BAwZg/Pjx6NGjhzAgKD4+Hv369cP27dthbGxciiX/vdy8eRPBwcFYsGAB3+8UQ/5zga+vLy5cuIC7d+/i6tWraNSoEYKDg2FmZoa7d+/CwcEBSkpKEIvF0NDQwIULF3j0/nc4d+4cXFxccOTIEVhbWwMAZs+ejbVr1+Lly5fw8PDAq1evkJWVBZFIhGPHjvF9JmOF4IA5++kVdjMquQAnJCRgzJgxmDx5MlxcXLiT/h0mTpyI0NBQdOzYEa9evcLdu3fRpk0bzJkzBwBw4MABrFq1Ck+ePEFwcDAaNGhQyiUue16/fo25c+ciISEBzZs3Fx7yhIeHY9GiRahVqxa8vLxgaWkJALh//z40NTWhoaEBkUjEN/2MMfYT+trD/RcvXqBfv37Q09NDYGAgAPANaRHl79ONHz8emzdvxvTp0+Hi4gJDQ0MAeWlxevfuDSsrK3h5ecHW1lbqGPxAv+Ru3ryJFStWoE+fPrC2tkZ4eDhWr16NihUrYubMmTA3Nxe2jYqKgru7O7frYggKCsL48ePRqVMn3L17Fx8+fMDMmTPRunVrAHlvJa5ZswZjx46Fo6MjqlevjiFDhiAzMxNHjx7ldl1K+JxSfP7+/pgyZQoiIyNhYGCA6OhohIeHIysrC2vWrIGFhQXu3buHmJgYyMjIoFevXpxatZgOHDiAixcvokWLFqhYsSLEYjFcXV2xceNG2NnZCdudPHlSeEOrcuXK+OOPP9C3b19+kM/Yl/w3qdIZ+z6JiYnk5uZGrq6uwkRC2dnZ1KpVK2rTpg3PCP+d1q5dS0ZGRnTu3DkiItq0aROVK1eO9PT0aNSoUcJ2kZGR5OXlxfVdTPlnGD98+DB17tyZjIyMaNGiRcLyLVu2kLW1NfXs2ZPOnj1b4Bhc54wx9vPK30+RTDL58uVLcnR0pJo1a/LEcMXw+eSn69atoypVqgh9FKK8SUBTU1OJiOj06dNkbGxMLi4udPPmzf+0rL+qHTt2kK6uLllZWdGrV6+E5Zs2bSJnZ2dq3769MNlnfvn7O+zLgoODSVZWliIiIoiI6OnTp2RqakpRUVGUmZkpbDd79myysrIiRUVFsrCwoIYNGwrnEu4Xsp9dbm4uZWdnU7du3Wjo0KFS66KiosjCwoIaN25c6HmbzyVFI7kWurq6kpmZGRkZGZGKigq5ubmRSCQid3d3On36NF28ePGrx+H6ZqxwPMKclRmSEVwyMjKYPHkyFi9ejFu3biEhIQHy8vL8xP87zJ07F5mZmZg9ezZ27dqFPn36YNKkSUhOTkZgYCCGDRsmjDSX4PouvnHjxuHy5cuQkZFBQkIClJWVMXDgQEyePBkAsHXrVixZsgSVKlUSRpwzxhgrGyT9FJFIhCFDhmD58uV4+vSpMDk5j976tm7duqFr167w8PAQRpiPHj0aKSkpCAsLw40bNxAXF4egoCCkpqbi77//RseOHRETE4PVq1cjPDyc+yY/wK5duxAYGIi4uDicPn1aajT5li1bsG7dOnz69AmhoaEwMDAoxZKWPVu3bkW3bt0QGhqKv/76S1hubW0NZWVlvHv3DjVq1EBQUBCqVq2K+/fv482bNyAi2NjYcJoh9lOT3B9SvjeE+vfvj4cPH+LAgQNS7dbLywuLFy+GoaEhDh48iJo1a5ZWscs8yTnhyZMnuHXrFt6+fYupU6fi3r17MDQ0RFJSEmrXrg11dXWYmZmhb9++QhozxtiX8ZWWlRmSiVfGjBkDZ2dnGBoa4urVq3wTWkzR0dE4dOgQ0tLS4OzsjK5du2LSpEl49OgRnj9/jqlTp2Ly5Mnw8vJCQkICgoODsWTJEmhoaMDLy0s4Dt+QFs+2bduwbt06REdHw8LCAmlpaZgwYQIiIyMhKyuLiRMnokuXLkhPT8fJkyc5NyVjjJUxkn7K6NGj4eHhgdq1a3OwvJgMDAzQsmVLAHl54MuVKwddXV1s2bIFXl5eiImJgYGBAdzd3fHy5Uv07dsXTZo0QdOmTdG0aVMA/EC/uAqrLw8PD5QvXx4fPnxAr169EBoaKuQt79atG9LT03H58mXUqFGjNIpcpqmqqgIAbt26hffv36N8+fLo0KEDkpKSMH78eKSnp2P58uXo3Lkz4uLiYGhoKKQgAvJ+X3wuYT+jLVu24MCBA5g4cSJ0dXVRvnx5AECDBg0QFxeH6OhoNGvWTJg82MzMDG5ubrCzs4O+vn4plrzsk6TD0tXVha6uLgDg1KlTkJOTw4QJE/DixQucO3cOJ06cQHJyMurUqVOaxWWszOAR5qzMuXXrFlauXInFixdzfrNiWrNmDSZPngx7e3s8evQIV69exerVq9GvXz8AwJEjRzB06FAcOXIEOjo6uHDhAvz8/ODp6YlOnTpxbsrv4Ofnh82bN+PChQtCe3369CmGDBmC+Ph4jB8/HmPGjJHah2/6GWOs7OF+SvF9fr1btWoViAh9+/bFixcvsG7dOvzzzz/o27cvWrRoAVNTUxw7dgzTp0/H9u3bUbly5VIsfdmVv96PHj2KzMxMZGdno23btgCA2NhY+Pn5ITk5GWvXrpUaaV7YMdiX5eTkQFZWFiKRCFFRUfD09MTEiRNx8+ZN3Lt3D7t37xaChuvXr0fv3r0RFxcnTCTM2M8sNTUVVlZWSEtLg7a2NqysrODo6Ii+ffsCADw9PXHt2jXMmjUL9vb2KF++PHr16gVLS0v4+PhAJBLx/B4/WGBgIHx9fXHx4kVUqFChwHqub8a+jXvvrMypXbs2/P39AYBvQoshODgYw4cPx+bNm9GhQwdcu3YNLVu2xKZNm9C1a1eoqKhAVVUV6enpCA8PR7du3TB9+nRUrVoVXbp04Y5MCUluJCtXrgyxWIynT59CX18fubm50NHRweTJk9GyZUv4+/tDXV0dffv2FV5j5BtQxhgre7ifUnySV/cl18y9e/fi5s2bUFVVRdeuXTF79mxMnDhRGJ2bk5ODv//+G2pqatDS0irNopdpkn6GZFJVJSUlvHjxAo0aNcLff/8NJycniMViLF68GAMHDkRAQADq169f6DHYl0lGkgPA5cuX0a5dO2zfvh2dOnWCgoICTp48CX19faH/V7FiRdSqVYsfBLEyQ1VVFZ07d0aNGjVgY2ODmJgYjB07Fvv27YOTkxO2bduGTp06wd/fH4MHD0a1atWQm5uLHTt2QCQSgYj4HvMHIiLUrl0b2dnZwvVVch8vGS/L9c3Yt3EPh5VpfBNaNLGxsRg4cCCmTJmCDh06AADMzc2hqKiI169f4+PHj0hJSYGtrS08PT0REBAAGxsbvHr1CqtXr+aOTDHk5uZK/Sy5kbS1tcXjx4/h7++P9PR0YXl2djYcHBwwZswY9O7dG8D/AgeMMcbKNu6nfFtubq5w3Xvw4AEAYM+ePbC3t8fcuXOxadMmfPjwAaqqqvjw4QMiIiLQokULvHjxAhERERCJRAWuvazogoODERYWht27d+PYsWO4evUqkpKSMGrUKCQmJqJZs2YYMWIEiAgrV64s7eKWOTExMejXrx8yMzMxcuRIdO3aFSkpKejQoQP27NmDrKwsbNmyBa9evRL+DlavXg0jIyPO6czKDFlZWTg6OmLChAmQk5ODl5cXXr58iTp16mDkyJFwcXGBra0tunfvjqCgIEyZMgXXr1+HnJwcxGIx3/v8YCKRCI0aNYK8vDzOnDkD4H8BcpFIxPXNWBFxL56x30D16tVhb2+P+Ph4XLhwAdbW1ujQoQOeP3+Oxo0bo0OHDkhLS4OHhwfq1q0LT09PAIC9vT1kZWV5hFwR5X8t+eTJk3j16hV0dHRgbGwMMzMzhISEoHPnzkhPT4e7uzv09fXh6+sLAwMDjBgxgkfxM8YY+63kv27OmjULe/bswZw5c9CiRQuEhYWhR48emD9/PgCgS5cuSE5OxtWrV2FsbIyAgABOeVNMBw8ehK2tLTQ1NYXRzDdv3kSTJk3QoEEDoQ9y7NgxWFlZwcfHB1u2bIGbmxsqVKgAa2vr0v4KZU5iYiKeP38OGxsbPH/+HOfOnYOmpibEYjFatWqFHTt2oGPHjiAiTJgwAX379sWdO3dw7do1yMjIcMobVma4ubmhZ8+eCAwMREBAABQVFbFjxw54eHigZs2aOH36NPbs2YNNmzahe/fuADgtyL/t48ePePToUWkXg7Eyi3OYM/abuHPnDkaOHAlZWVmkpqYiPT0dYWFhMDMzw7Vr15CYmAg/Pz88fPgQLVu2RFhYGADuyBRV/tngvb29sXPnTmRmZkJPTw+6urpYvHgxqlWrhv3798PLywupqamQk5ODlpYWTp06BXl5ealjMMYYY7+LKVOmIDg4GEFBQTA3N5caWduzZ0/Ex8fD29sb3bp1w6dPn6CsrMwPmYspKCgIY8eOxcKFC9GtWzeoq6uDiNC9e3ckJSXh0KFDAIDMzEwoKipi586dGDlyJE6dOiU1uScHcIuva9eu2LZtG1q2bIkNGzagQoUKwqhaGRkZREZGonPnzpCVlYWJiQni4+N5smBWJq1duxYhISH4559/0Lx5cygrK2Pfvn1QU1PDy5cvcfz4cXh6enK7/o9s2rQJXbp04fpmrIQ4YM7Yb+TOnTsYOnQozp8/j6CgIHTu3BnA/25+MjIy8OjRIxgbG/MNaAn5+flh6dKl2LZtG+zt7eHl5YWAgADY29tj7dq10NPTw/Pnz/HhwwekpKTAxsYGMjIyfFPEGGPst3Tr1i106NABfn5+aN26tbA8Ozsb8vLyAIC//voL//zzDzZv3oxWrVoBAD9kLoGhQ4fi0KFDGDt2LLp06YIKFSrgwIED8PT0hL+/P/r37y9su23bNsydOxexsbHQ1NQsxVKXPZK2mZ2dDbFYjMDAQLx79w7Hjh1DlSpV4Ovrixo1auDTp08oV64cACAqKgp+fn44duwYB8tZmWZra4sLFy7A0dERERERhU44ye37v8X1zVjJcMCcsd/MvXv3MGzYMMjIyGDy5Mmwt7cHUPBCyqO2iib/SKuXL1+iW7duGDZsGDp27IgDBw6gU6dO6NatG86dO4cqVaogJCQEVatWlToG1zVjjLHf1fHjx+Hh4YHz58/DyMhIKhCekZEBJSUlAMDMmTMxdepUvl6WQFZWFhQUFAAAgwYNwsmTJzF06FB0794d5cqVw/Tp07Fz506MGzcOvXr1QmpqKgYNGgQiwp49e/jBRDHk7xfmb78AEBAQgK1bt0JHRwd///039PT0AAAnTpwQ+uMAB7dY2SQ5d2/cuBHz589HaGgorKys+OEmY6zM4vfpGPvN1KxZE8uXLwcRYe7cuTh58iSAghOT8Q3ptxGRcFN05MgRVKhQAZMmTULDhg1x7tw59O/fHwsXLkRQUBAcHBwQHR2NVq1a4fXr11LH4bpmjDH2O8g/Tkfybw0NDWhoaODGjRsAIKRaAYDt27dj586dAAAfHx/IysoK61jREJEQLA8LC4OhoSHu3r2LWbNmYcuWLZCVlcWYMWPQo0cPjB8/HrVr14azszNevnyJqKgonlS1mCT9wr///hutWrWCh4cHVq1aBQAYNmwYunbtiufPn2P06NE4deoUXF1d4e3tLfW3wcFyVhZJguLOzs5ITk4W0jxxsJwxVlZxwJyx35CxsTH8/f0hKyuL0aNH48qVK6VdpDIn/2iJadOmYdSoUXj48CFatGgBXV1d7Nu3D05OTujTpw+AvAcVbm5uaNWqFSpWrFiaRWeMMcb+c7m5uVKBk8zMTACAvr4+KlWqhOXLl+PatWsAIEw4vmXLFkRHR0sdhx8yF4+kzmfMmIHRo0dDV1cXQUFBaNSoEaZPn45169ahYsWKmDVrFq5evYqlS5dixYoVOHv2rJAahHOWf1v+hwoLFizAwoUL0bBhQygpKcHb2xtTpkwBkJcWp2fPnnj37h06d+6MjIwMxMTEcFCR/TKqV6+OSZMmYeHChcKDUMYYK4v48TVjvyljY2MsWLAAwcHBMDc3L+3ilDmSG5uHDx/i2rVr8Pf3h4mJibD+zZs3uHr1KrKzs1GuXDnExcXBxcUFY8aMAcBpWBhjjP0+8qepWLRoEeLj43Hx4kX069cPXbt2xc6dO9G4cWOMHDkSdnZ2qFGjBjZv3oy3b99i9+7dpVz6so2I8PbtW+zYsQNz587Fn3/+CSAvL3yfPn3g7e0NAOjUqRNq1qwpNeGqWCzm0c5FJGnf8fHxUFFRwebNm9GiRQukpqZi06ZNGDlyJABg7ty56NevH1q0aIGUlBSYm5vzXDbsl9OqVStcuHABtWvXLu2iMMZYifFVmbHfmKmpKRYtWgRA+maWfVn+keXLly/H4sWLoa2tDQMDAwD/q0dnZ2dcuHAB1tbWUFFRwcePH7F161bhGBwsZ4wx9ruQ9C8mT56MdevWYeLEiXBycsLEiRNx6tQpREZGIjY2FvPmzcPevXuhqqoKfX19HDp0CHJycvyQ+TuIRCLIy8tL9T0yMzOhqKiIkJAQ3LhxA8uWLcP79+8xZMgQqKqqCvtynRfP8ePH4eTkhAoVKiAqKgoAoK6ujl69egEARo8eDRkZGcyePRu6urrQ1dUFwA8m2K+nZs2aCA0NFVJs8bmEMVYW8ZWZMQYAHCwvgri4OJw/fx4AMHjwYHTs2BFLly7FuXPncOvWLRgYGAj16OHhASJCQkICcnNzMWvWLL7pZ4wx9tuKj49HZGQkIiMjYWdnhwsXLuD9+/do3749gLwAy9q1a5GdnY3MzEyUL18eAE+AWFyFDYBQU1ND9erVsX79egwaNAiKiorIzs6GvLw8DAwMcPz4cSQkJEBFRaWUSv1r0NfXh4+PD/z8/HDmzBlhIk8VFRX06tULMjIyGDp0KGrUqIH+/fsL+3G/kP2KJAOMuH0zxsoq7n0yxlgRbNiwAXPmzIGbmxvMzMygrKwMZWVlxMfHw9raGtOnT4eenh7q1KkDIG/Cpo4dO6Jjx47CMThYzhhj7HeVk5MDZWVl2NnZYdu2bejXrx+WL1+Ov/76Cx8+fMCZM2dgZ2cHFRUVyMvLA8h7I4uD5UWXP1iekJAAVVVVqKiooGrVqliyZAmaN28OT09PRERECP0RkUiE7du3w87ODiKRSOpNOvZlhT2Y0NPTw8iRI5GZmYmpU6dCRUUFQ4YMAZAXNO/Rowe0tbXh7u5eGkVmjDHGWDFwD5Qxxr5hw4YNGDRoEAIDA9G+fXvhdWU/Pz84ODggPj4elpaWGDRoEIKCgmBmZgag4M0UB8sZY4z9Dl6+fImkpCRcvnwZlpaWqF69OpSUlPDkyROsWbMG48ePx/z584Vg4tmzZ7Fq1Sro6elJzQfCgdvikfQ5Jk6ciK1btyI1NRXNmjVD79690aZNGwQHB2PgwIGoXbs2jI2N8fz5c7x//x6NGjWCjIwMp+crovz1tHLlSty+fRu3b99G//794ejoCB8fH8jJyWHixIkQiUQYPHgwAEBVVVV4o4LfnGCMMcZ+btwjYoyxr7h58yYWLFiApUuXomfPnkKwvHPnzvD29sa0adOQmJiIhIQEPH/+HIMHD8bly5cBcJobxhhjv5+IiAhhUsNhw4bB1tYWvXv3Rnp6Orp06YJBgwZhzJgxGDp0KAAgKysLS5cuhUgkgpGRUSmXvmwiIuHfMTEx2LFjB8LCwrB06VLIyspi5syZiIyMROvWrXHp0iW0adMG+vr6cHJywo0bNyArKwuxWMz9liLK/2BixowZ0NLSgq6uLiZOnIhJkyZBTk4OQ4cOxejRozFp0iQsXLiwwDE4WM4YY4z93ESUv4fFGGNMSnR0NAYNGoT9+/fDxMQEMjIyGDZsGKKjo7Fs2TIsWbIEMjIymDVrFmrXrg0tLS0MGDAAAQEBpV10xhhj7D+1Zs0aTJw4EVOmTIGlpSWsrKywfPlybN68GUSELl26IDExEadPn8asWbPw9u1b7N+/H8+ePcOlS5cgLy/Po5y/Q2RkJKKjo1GjRg14e3sDAC5evIhly5bh+vXrGD9+PLp06VJgPx7tXHyxsbEYMGAAwsPDYWVlhZiYGLi6uiI0NBTdu3cHALx9+xYzZ87EjRs3EB0dzW9MMMYYY2UIB8wZY+wr5s6diyVLluDNmzfCshcvXkAsFkNHRwc3b97EgAED8OnTJ5w9exYpKSlQV1fn9CuMMcZ+K2vWrMHw4cOxZcsWeHp6Sq3bunUrFi5cCGVlZQwdOhTHjx/H7t27YWRkBENDQ6xevRpycnIcuP0O9+/fR58+fXDlyhX0798fCxYsENZdvHgR/v7+uHnzJgYNGoS+ffuWYknLnlmzZqFjx45Cyj0A2L17N3x9fXHq1Cls3boVAwYMENIMffjwAZcuXYKDgwPevn0LTU1Nzg/PGGOMlTE8fIMxxr7CyMgIGRkZOHTokLCsatWq0NHRQW5uLkxNTdG2bVtoaWkhLS0NFSpUEF5tZowxxn4HsbGxGDRoEKZMmQJPT08QEYgIOTk5AIAuXbqgZ8+euHz5MuTl5REQEID4+HjExMQgODiYg+U/gKGhIaZPnw4bGxtERkbi8OHDwroGDRpg1KhR0NbWxsmTJ0uxlGXPkSNHcOvWLanc+gCQlJQEIsKhQ4cwcOBA+Pr6Cjn5Y2JiEB4ejhcvXqBChQocLGeMMcbKIB5hzhhjX3H//n3Ur18fLi4uWLx4MfT09KTWv3//Hn/++SeMjY2xePHiUiolY4wxVnru3LmDfv36oUKFChg3bhwcHByEdflTrFhYWMDBwQErV65EdnY25OXlAYCDicWUv04/r7vDhw9jyZIlyM7Ohre3N5o2bSqsS0xMhJGREae8KSZJW921axcqV64MOzs7fPz4Eebm5nj06BHCwsLQs2dPAHk5+T09PaGpqYkNGzZwu2aMMcbKKO4tMcbYV0heFd+zZw8mT56MhIQEYd2jR4/QoUMHPHnyBH5+fgCkJ95ijDHGfgfGxsZYu3YtsrKyMHfuXJw4cUJYJwkYpqWlITMzE1WrVgUAIViefxv2bfmD5evWrcOQIUMwcuRIbN68GQDQvHlzDB8+HOXKlcPff/+No0ePCvtK5mLJzc0tlbKXJd7e3kIeeHl5eVy7dg3jxo3D8uXLcf78eaioqGDx4sWoXr06tmzZgpiYGGzfvh0eHh54/PgxQkNDhZHljDHGGCt7OGDOGGPf0LlzZ6xYsQLbtm2Du7s7WrZsCRcXF3Tp0gVpaWk4f/485OTkIBaL+aafMcbYb8nY2Bj+/v4QiUSYM2dOgdQf9+/fh46ODho1agSAHzCXlCRYPnHiREyaNAmysrJ4/vw5/Pz8MHXqVABAy5YtMXz4cCgpKWHcuHG4ePFiocdghUtNTcWLFy9w7NgxzJ8/HwBgbm6O2bNn4/79+1i2bBmuXLmC9u3bY+PGjXj16hX69OmDBQsWQF1dHRcvXuR+IWOMMVbGcUoWxhgrooSEBAQHByMxMRF6enpo0KABBg0aBFlZWc69yhhjjCEvPcvIkSNBRJgyZQocHByQk5MDDw8PyMjIYNeuXRyw/U7r1q2Dr68vNm/eDBsbG2zevBl9+vRBlSpV0L59eyxduhQAEBUVhZMnT2L+/Plc58X0+vVrzJ07FwkJCWjevDmmTZsGAAgPD8eiRYtQq1YteHl5wdLSEkDeAyFNTU1oaGhAJBJxv5Axxhgr4zhgzhhj30ksFkNWVra0i8EYY4z9FCRBcxkZGUyePBmLFy/GrVu3kJCQAHl5eam0Iqz45s6di8zMTMyePRu7du1Cnz59MGnSJCQnJyMwMBDDhg3DnDlzpPbhOi+a/H26I0eOICgoCBcvXsSQIUMwduxYAP8LmpuammL48OGwtbWVOgbXNWOMMVb2ccCcMcaKgScmY4wxxr7tzp07GDNmDKKjo2FoaIirV69CXl6eR94WU3R0NA4dOoS0tDQ4Ozuja9euyM3NxaNHj6CgoABXV1f06tULXl5ewmjojIwMzJw5E15eXqVd/DJr3LhxuHz5MmRkZJCQkABlZWUMHDgQkydPBgBs3boVS5YsQaVKlYQR54wxxhj7dXBvlTHGioGD5Ywxxti3GRsbY+HChVi5ciUWL14MOTk5DpYX05o1azB58mTY29vj0aNHWLduHT5+/Ih+/frBwMAAR44cwadPn9C1a1cAQE5ODpo2bQpPT0906tSplEtfdm3btg3r1q1DdHQ0LCwskJaWhgkTJiAyMhKysrKYOHEiunTpgvT0dJw8eRLGxsalXWTGGGOM/WD8rhhjjDHGGGPsh6tduzb8/f05WF4CwcHBGD58OFavXo3IyEisX78eVapUwaZNm/Dx40cAgKqqKtLT0xEeHo5nz55h+vTpKF++PLp06QJZWVmIxeJS/hZl08OHD1GjRg3Ur18fioqKqFy5MubMmYMqVapg2bJlWLJkCQCgT58+CA4OhoyMDHJzc0u51Iwxxhj7kThgzhhjjDHGGPtXcbC86GJjYzFw4EBMmTIFHTp0AACYm5tDUVERr1+/xsePH5GSkgJbW1t4enoiICAANjY2ePXqFVavXg2RSAQi4vlVikkS9K5cuTLEYjGePn0qLNfR0cHkyZORnp4Of39/rFu3DkBeqj4AnLOcMcYY+8Vwz5UxxhhjjDHGfhLVq1eHvb094uPjceHCBVhbW6NDhw54/vw5GjdujA4dOiAtLQ0eHh6oW7cuPD09AQD29vaQlZXl0fxF9PnknJJ/29ra4vHjx/D398ecOXOgrKwMAMjOzoaDgwNcXFzQu3dvAJyqjzHGGPtV8aSfjDHGGGOMMfYTuXPnDkaOHAlZWVmkpqYiPT0dYWFhMDMzw7Vr15CYmAg/Pz88fPgQLVu2RFhYGABALBbzyPIiyB8sP3nyJF69egUdHR0YGxtDU1MTERER6Ny5M/r37w93d3fo6+vDy8sLBgYGCAgIgEgk4rpmjDHGfmEcMGeMMcYYY4yxn8ydO3cwdOhQnD9/HkFBQejcuTOA/wV7MzIy8OjRIxgbG3PgthiISBgZ7u3tjZ07dyIzMxN6enrQ1dXF4sWLUa1aNezfvx9eXl5ITU2FnJwctLS0cOrUKcjLy0sdgzHGGGO/Hg6YM8YYY4wxxthP6N69exg2bBhkZGQwefJk2NvbA0CBtCs82rn4/Pz8sHTpUmzbtg329vbw8vJCQEAA7O3tsXbtWujp6eH58+f48OEDUlJSYGNjAxkZGU55wxhjjP0GOGDOGGOMMcYYYz8pSXoWAJg6dSoaN25cyiUqm/KnYXn58iW6deuGYcOGoWPHjjhw4AA6deqEbt264dy5c6hSpQpCQkJQtWpVqWPwgwnGGGPs98DTeTPGGGOMMcbYT8rY2Bj+/v6QlZXF6NGjceXKldIuUplDREKw/MiRI6hQoQImTZqEhg0b4ty5c+jfvz8WLlyIoKAgODg4IDo6Gq1atcLr16+ljsPBcsYYY+z3wAFzxhhjjDHGGPuJGRsbY8GCBXB0dIS5uXlpF6dMyZ9vfNq0aRg1ahQePnyIFi1aQFdXF/v27YOTkxP69OkDAKhZsybc3NzQqlUrVKxYsTSLzhhjjLFSwsnXGGOMMcYYY+wnZ2pqikWLFgGQTi/Cvk4SLH/48CGuXbsGf39/mJiYCOvfvHmDq1evIjs7G+XKlUNcXBxcXFwwZswYAJyGhTHGGPsdccCcMcYYY4wxxsoQDpZ/W/6R5cuXL8fixYuhra0NAwMDAP976ODs7IwLFy7A2toaKioq+PjxI7Zu3Socg4PljDHG2O+HJ/1kjDHGGGOMMfbLiIuLw/nz5wEAgwcPRmpqKuzt7fHgwQPs3bsXLVu2FLbNyclBVFQUEhISkJubi1mzZkFOTo5HljPGGGO/MQ6YM8YYY4wxxhj7JWzYsAFz5syBm5sbzMzMMGjQIADAu3fvYG1tDU1NTYSGhqJOnTpfPAYHyxljjLHfGwfMGWOMMcYYY4yVeRs2bMCgQYMQGBiI9u3bQ1VVFQDg5+cHBwcHmJmZwdLSEtWrV0dQUBDMzMwAcE54xhhjjEnjgDljjDHGGGOMsTLt5s2b6NKlC4YPH46BAwcKyzt37owdO3agadOm8PX1hYmJCerXrw8dHR0sX74c9erVK8VSM8YYY+xnxI/RGWOMMcYYY4yVaU+ePMH79+/h6OiI3NxcAMCwYcNw6dIl7NmzByKRCFOnTsWtW7dw6dIlnDlzBkFBQaVcasYYY4z9jHiEOWOMMcYYY4yxMm3u3LlYsmQJ3rx5Iyx78eIFxGIxdHR0cPPmTQwYMACfPn3C2bNnkZKSAnV1dc5VzhhjjLECeIQ5Y4wxxhhjjLEyzcjICBkZGTh06JCwrGrVqtDR0UFubi5MTU3Rtm1baGlpIS0tDRUqVICsrCzEYnEplpoxxhhjPyMOmDPGGGOMMcYYK9NsbGwgJyeHwMBAPH78WGqdjIwM3r9/j7i4ONSqVQvq6urCOh5hzhhjjLHPyZV2ARhjjDHGGGOMse9haGiI1atXo0+fPlBUVISXlxcsLS0BAI8ePcKAAQPw+vVrREZGAgCICCKRqBRLzBhjjLGfFecwZ4wxxhhjjDFW5onFYoSEhGDo0KHQ1taGubk5cnJy8P79ewBAXFwc5OXlIRaLeWQ5Y4wxxr6IA+aMMcYYY4wxxn4ZCQkJCA4ORmJiIvT09NCgQQMMGjQIsrKyyMnJgZwcv2jNGGOMsS/jgDljjDHGGGOMsV8ejyxnjDHGWFFwwJwxxhhjjDHG2C+Fc5QzxhhjrKRkSrsAjDHGGGOMMcbYj8TBcsYYY4yVFAfMGWOMMcYYY4wxxhhjjDFwwJwxxhhjjDHGGGOMMcYYA8ABc8YYY4wxxhhjjDHGGGMMAAfMGWOMMcYYY4wxxhhjjDEAHDBnjDHGGGOMMcYYY4wxxgBwwJwxxhhjjDHGGGOMMcYYA8ABc8YYY4wxxhhjjDHGGGMMAAfMGWOMMcYYY4wxxhhjjDEAHDBnjDHGGGOMMcYYY4wxxgBwwJwxxhhjjDHGGGOMMcYYA8ABc8YYY4wxxhhjjDHGGGMMAAfMGWOMMcYYY4wxxhhjjDEAHDBnjDHGGGOMMcYYY4wxxgBwwJwxxhhjjDHGGGOMMcYYA8ABc8YYY4wxxhhjjDHGGGMMAAfMGWOMMcYYY4wxxhhjjDEAHDBnjDHGGGOMMcYYY4wxxgBwwJwxxhhjjDHGGGOMMcYYA8ABc8YYY4wxxhhjjDHGGGMMAAfMGWOMMVaGxcbGQiQSYcaMGVLL9fX1oa+vX6xjOTk5QSQS/bjC/QsePnwIkUiE3r17l3ZRAJSsnn9X31NXoaGhEIlECA0N/SFlGT58OLS0tPDhw4cfcryfTa9evVCjRg1kZmZ+97FSUlIQGRmJZcuWYcGCBdiyZQvu3r37A0r5a/jV29J/4d++9hR2nczJyYGRkRE6d+78r30uY4wxVpZxwJwxxhhjBcTHx6Nfv34wNjaGiooKlJSUULNmTfTs2ROHDh0q7eKVyIwZMyASiRAbG1vaRfnXvXv3DsrKytDU1Pxm0HD58uUQiUQYOXLkf1S6n8OXHrZ8iyR4nf8/JSUlmJiYYMSIEXj58uW/U+AfJDExEYGBgZgwYQJUVVULrCciREREwNPTEzo6OlBQUED58uVRr149jBkzBjdu3AAAHDx4ECKRCG5ubt/8zA4dOkAkEiEiIqJYZZU8IMr/n7y8PKpXr47OnTvjwoULhe43bdo0PHv2DEuWLCnW5+V38+ZNeHp6onLlyvD09MTo0aMxYcIE/PnnnzA2Nkbjxo0RFxdXomOnpKTAy8sLRkZGUFBQgJaWFjp27Ijr168Xur0koFrYf4XV/9OnT+Hp6YkKFSpAV1cX48ePR1ZWVoHtsrKyUKtWLXh6epboe3zeliR/U0X9z8nJqUSf+zW9e/eGSCTCw4cPi7Xf53UsIyMDTU1NODo6IjQ0FET0w8tamuTk5DBlyhRs374dp06dKu3iMMYYYz8dudIuAGOMMcZ+Hrm5ufDy8sKSJUsgJyeHpk2bom3btpCXl8f9+/exd+9ebNy4EbNmzcK0adNKu7hfdOTIkWLvs379eqSnp/8LpfnvaWhowNPTE5s2bUJkZCS6dev2xW3XrVsHAOjXr99/VbxfQrNmzWBvbw8AePPmDWJiYrBixQpERUXh4sWL0NLSktq+JG3y3zBz5kwoKChgyJAhBda9ffsWnTp1QkxMDDQ0NODi4gJDQ0N8+vQJ169fx8qVK+Hv748jR47AxcUFenp6OHToEJ4+fQodHZ1CP+/NmzfYvXs3tLS04O7uXqIy16xZEz169AAAfPz4EfHx8di+fTuioqJw+PBhODo6Sm1vZGSEdu3aYf78+Rg5ciRUVFSK9XkhISEYPHgwNDU1MXXqVLRu3Rq1a9eGrKwsnj17hujoaKxevRpNmjTB5MmTMWfOnCIfOzk5GXZ2drhz5w7s7Ozg4eGBFy9eYOfOndi/fz9iYmLQsGHDQvf18fEpsMzIyEjqZ7FYjDZt2uDOnTvo06cPkpKSsHDhQmRnZ2Pp0qVS286ePRsvX77E0aNHi1z+/D5vS/r6+gXK+PDhQ4SFhaFevXpo166d1Lqf8e2UcePGQVVVFWKxGPfv30dERATi4uIQHx+P5cuX/yufWVrXnp49e2LSpEmYPn06Dh8+/J9/PmOMMfZTI8YYY4yx/zdp0iQCQJaWlnT37t0C69PT08nPz48mTpxYCqUr6OjRowSAfHx8vrmtj48PAaCjR4/+6+X6tzx48IAAUK9evb65bUxMDAGg5s2bf3GbS5cuEQCysrIqUXlq1KhBNWrUKNG+pa04bSe/kJAQAkC+vr5Sy8ViMbVq1YoA0PTp039gSf/3mSEhId91nKSkJFJQUKAePXoUWJednU2Ojo4EgHr06EGpqakFtnn+/Dn16dOHoqKiiIho+vTpBIDmzJnzxc9cvHgxAaBx48YVu7yS9u7q6lpgna+vLwEgR0fHQveNjIwkALRmzZpifea6desIAHl6ehZaBxLZ2dk0c+ZMAkCzZ88u8vGHDRtGAGjs2LFSy0+dOkWysrJkZmZGYrFYal2TJk2oqLdtp06dIgC0YcMGYVnfvn1JSUmJcnNzhWXXrl0jeXl5WrVqVZHLnt/X2lJ+kr+zopyzfoRevXoRAHrw4EGx9pPU8YsXL6SWX7lyhZSUlEgkEtH9+/d/YEn/O187140aNYpEIhElJib+9wVjjDHGfmKckoUxxhhjAIC7d+/Cz88PFStWxIEDB1CzZs0C2ygpKWH8+PGYOXOm1PLk5GSMGTMGBgYGUFBQQOXKldGlSxchfUN++V+ZX7lyJUxNTaGoqIgaNWpg5syZyM3NLbBPRkYGvL29oaurC0VFRZibm2PNmjVf/C6f54t2cnISyuzs7Cy8dv/5NoXlkc3JycGSJUtQr149KCkpQV1dHc7Ozti7d2+BbfPnmj5y5Ajs7e2hoqKCihUrolevXkhOTi6wz7p16+Dh4QF9fX0oKiqiQoUKcHV1LfGoz/zfp2bNmoiJicHjx48L3Wbt2rUA/je6/OjRo+jbty9q1aoFVVVVqKqqwtraGkFBQUX+3K+lRPhaWpzjx4/D3d0dlSpVgoKCAoyNjTF16tRijbwsal3OmDEDzs7OAPJGyeZPxVDcVA4SMjIyQm75+Pj4AusLy2GemZmJRYsWoV69elBXV4eqqipq1qyJbt264erVq9/8zCdPnsDU1BTKysrYs2fPN7ffsmULsrKy0KlTpwLrNmzYgOPHj8PR0RFhYWFQU1MrsE3VqlWxbt06IQ1Inz59vplbPSQkBADQt29fAD+uvUvabGF1DQCtWrWCioqK8PlFcfv2bQwZMgR//fUXduzYUWgdSMjJyWH69OmYOXMmfHx8cP78+SJ9RlRUFGRkZAqcQ+3s7ODu7o4bN27g2LFjRS7z5548eQIAsLa2FpZZW1sjIyMDSUlJAPLS7gwcOBC2trYYNGhQiT7na22pqN6/fw8fHx/UqVMHSkpK0NDQgJubG06cOFFg2xcvXmDUqFEwNjaGkpISKlSoAAsLCwwdOhRpaWkA8v7GwsLCAAAGBgY/JPWLhYUFmjRpAiKSamsPHjxA//79oaenBwUFBVStWhW9e/fGo0ePChxDUoZnz56hd+/eqFKlCmRkZITz4I+49gDFv04CQOfOnUFEP2x+BMYYY+xXwSlZGGOMMQYgL9grFosxaNAgaGtrf3VbBQUF4d/Jyclo1KgR7t69CycnJ3Tt2hUPHz7Ejh07sHfvXhw6dAh2dnYFjjF+/HjExsaiTZs2aNGiBaKiojBjxgx8+vQJc+fOFbbLzc1F27ZtcfjwYVhYWODPP/8UAvSSoOe3SAKZx44dQ69evYTApYaGxlf3IyJ06dIFERERMDExwbBhw/Dx40ds27YNbdq0wbJlywrN/b17927s2bMH7u7uGDJkCI4fP47169fj3r17BYJBw4YNQ7169dC8eXNoaWnh2bNniIqKQvPmzREREQEPD48ifcfPiUQi9OnTB1OnTkVoaCimT58utf7Tp0/YvHkzlJSUhJQt8+fPx927d9GoUSO0b98e7969w4EDBzBo0CDcvn0bixYtKlFZvmX16tUYOnQoNDU14e7uDi0tLZw/fx5z587F0aNHcfToUZQrV+6bxylqXTo5OQmpIpo0aSIVUPtWm/ga+v88x3JyReti9+rVC9u2bUPdunXRp08fKCgo4PHjxzh69ChcXV1hYWHxxX1v3LgBV1dXfPz4EYcOHULjxo2/+XmStDCNGjUqsE7y8GTq1KmQkfn6mBrJ37++vj6aNWuGw4cPC8H2/C5cuICrV6/Czs4OZmZmAH58e/9SXZcrVw5WVlY4deoUPn78WKS0LD4+PqhWrRoCAgKEAOapU6cwduxYJCQkoHLlyhgwYAD09PTQu3dvEBGmTp2KqKgo+Pr6FilH+6tXr1CpUqVC88cbGBgAAGJiYgo9t4WHh+PBgwdQUVGBjY1NoedVXV1dAEBCQgJq164NALh06RKUlJSENEGrVq3ChQsXkJCQUOLJJr/Wlori7du3cHR0xPXr1+Hg4ABXV1ekpqZi165dcHZ2xvbt24UULunp6WjcuDEePnyIFi1aoH379vj06RPu37+P0NBQTJgwAWpqahg9ejRCQ0Nx+fJljBo1Svhb/tGpX86ePSv87bm7u8PIyAgPHz7Epk2bsH//fpw+fRqGhoZS+0hS8VSoUAFdunTBp0+fvvpAprjXnpJeJ62srFCuXDnExMR8f8Uwxhhjv5JSHN3OGGOMsZ+Ik5MTAaDDhw8Xa7++ffsSAJo0aZLU8gMHDhAAMjY2lkoxIHll3sDAgJ4/fy4sT0pKIg0NDSpfvjxlZWUJyyXpKNzc3CgnJ0dYfuXKFSpXrlyhr5oXlirkWylZCkt7sH79egJATZo0kSrTkydPqHLlyiQvLy/1mr6krHJycnTixAlheU5OjlC/p0+flvqMwl7zf/78OVWrVo2MjY2llhcnJQsR0dOnT0lWVpYMDAyk0jEQEW3dulVIv/G1smRnZ5OLiwvJysrSo0ePpNYVVs9fS4lQ2O/g+vXrJCcnR/Xr16fk5GSp7SVpNxYuXFik71ucuvzRKVlycnLI1dWVANCCBQsK7Pd5Xb17945EIhFZW1tLtWvJsVJSUgp8piQly6lTp6hChQpUrVo1unr1apHLrqWlRdWrVy+wPDs7m+Tl5UlOTo4yMjKKfDwioi1bthAA6t27d4F1Q4YMIQAUHBwsLCtJey8sJcvs2bMJALVu3fqLZRszZgwBoJiYmG9+j3fv3pG8vDwtXbpUWHb9+nVSVlYmTU1NGjBgAA0cOJA0NDTI0NBQ6lyxatUqUlBQoMzMzG9+TtWqVUlGRobev39fYF27du0IAHXu3FlqueTc9Pl/NjY2BeozJyeH6tatS2pqajRixAjq1q0biUQiGjFiBBERPXv2jNTU1GjGjBnfLOvXfKktfe5LKVn+/PNPAkDr1q2TWv7y5UvS1dUlLS0toS3+888/BIDGjBlT4PhpaWlS5+Z/MyXLgwcP6NOnT6Svr0/ly5enhIQEqW3j4uJIVlaW2rRpI7Vc8vvq06dPgb/1/J+dX0mvPcW5TkrUr1+f5OXli9R+GWOMsd8Fp2RhjDHGGADg5cuXAPDFyfsK8+nTJ2zZsgUVK1bE1KlTpda5urrC1dUVd+7cwalTpwrsO23aNFStWlX4uVKlSvDw8MD79+9x+/ZtYfn69esBAHPnzoWsrKyw3MLCAj179ixyWUtC8pq6n5+f1AhnHR0djBkzBtnZ2di0aVOB/f7880+pEb+ysrLo1asXABRI3SAZVZpf1apV0aFDB9y5c6fQV/yLqnr16mjRogUePHhQIA1KYZN9FlYWOTk5DB48GGKx+LvTxBQmMDAQOTk58Pf3R4UKFaTWTZgwAVpaWtiyZUuRjvVv1uXnDh8+jBkzZmDGjBkYMWIE6tSpg4MHD6JRo0aFTqj5OZFIBCKCgoKCVLsG8trLl0a679u3D82bN0elSpVw6tQpmJubF6m8nz59QlJSUqFvjyQnJyM7OxuVKlWCoqJikY4n0b59e2hqamL79u348OGDsDwzMxNbtmyBiooKunTpIiwvye/o7t27Ql2PHz8eTk5OmDZtGipXrowFCxZ8sWyS7/r06dNvfo/Y2FhkZ2ejc+fOwrLZs2eDiHD27FkEBQUhMDAQZ86cwbNnz6T2tbW1RVZWltR560tatmyJ3NzcAilZzp07J6TVeffundS6du3aYf/+/Xjx4gU+fvyIhIQE/PXXXzh//jyaN28ulbZIVlYWu3fvhrOzMzZu3Ijjx49j9OjR8PPzAwAMHz4c1atXx6RJkxAfH49GjRpBXl4e1apVw+LFi79ZfuDrbako3rx5g61bt6JZs2bo06eP1DptbW2MHz8eSUlJBSaiVFJSKnCs8uXLF+ntk6JauHAhZsyYgWnTpqF79+6wtbVFRkYGRowYAX19fezZswcPHz7EhAkTUK9ePal97e3t4eHhgX379glpYiTKlSsHPz+/An/rX1Lca8/3XCe1tbWRnZ2N169fF6lsjDHG2O+AU7IwxhhjrMRu3bqFjIwMODk5QVlZucB6JycnHDx4EAkJCbC3t5da16BBgwLbS4L1+QNGly9fhrKycqHbOzg4CKkk/g2SVAa2trYF1knSeCQkJBRYV9TvBgD379+Hr68vYmJi8OzZM2RlZUmtf/78OWrUqFGyL4C8gPj+/fsREhIivJr/9OlTHDp0CIaGhmjSpImw7fv377Fw4UJERUXh3r17+PjxY4Gy/GhnzpwBABw4cKBAgAwA5OXlcevWrSId69+uy/yOHDkipKWQsLOzQ0xMTJGCzmpqanBzc8OBAwfQoEEDdOzYEQ4ODmjYsOEXA4Dbt29HdHQ06tevj3379qFSpUpFLq8kf76mpmaR9ykKBQUFdO/eHStWrMC2bduEXOURERF49+4d+vTpI5V+pCS/o3v37hUIMFeuXBlxcXEwMTH5YtkkD2DevHnzze/x6NEjKCsrSz3EO3r0KNq3bw9jY2NhWa1atdC+fXuEh4cLyyTnvvwPDL5k5syZ2L9/PxYuXIjTp0+jUaNGePHiBXbs2AEzMzNcuXKlQFB19OjRUj/Xq1cPYWFhyMnJwebNmxESEoJhw4YJ6/X09BAVFVXgs3ft2oWoqCjExcXh06dPaNWqFUxNTbFv3z6cOnUK48aNg56eHjp27PjV7/C9ben8+fMQi8XIzMzEjBkzCqy/c+cOgLzrS5s2beDo6IgqVarA19cXCQkJaN26Nezt7WFhYVHilDJfIkk7JRKJoKamBhsbG/Tr1w9//fUXgP+dr27dulVo2V++fInc3FwkJiZK5ZE3MDAo1t9rca8933OdzP93IknpwxhjjP3uOGDOGGOMMQBAlSpVcOvWLTx79gy1atUq0j6SUXRfGmlYpUoVAEBqamqBderq6gWWSfIRi8ViYVlqauoXb+JLOsKxqNLS0r742T/iu929exe2trZIS0uDs7Mz3N3doaamJkwId+zYsQIBxeJq27YttLS0sHPnTqxYsQJqamoIDQ1Fbm4u+vbtKwScPn36BCcnJ1y8eBH169dHz549UbFiRcjJyQn5vr+3LIV5+/YtAEjlrS+J/6Iu8/P19YW3tzdyc3Px8OFDzJgxAxs2bMCAAQOwYcOGIh1jx44dmDdvHrZs2YIpU6YAyBsx27dvX8ybN6/AQ6jTp08jJycHDg4OxQq+Af8bnZuRkVFgXcWKFSEvL4/k5GRkZWVJzVFQFP369cOKFSsQEhIiNbmnZJ1ESX9Hrq6uOHDgAAAgKSkJYWFhmDhxItq1a4dz584Vmg88/3ct7GHe5z5+/Fggp/SXAoh6enpSP0tGsEtyhH+Njo4Ozp8/Dx8fH+zfvx/nzp2Drq4uZs2aBX19fXTt2rVIxwHy6nbz5s04efKkVMC8MO/fv8fw4cMxaNAgNG7cGIGBgUhKSsKFCxegq6sLFxcXHD9+HIsXL/5mwPxrbakoJH/zJ0+exMmTJ7+4neSBnbq6Ok6fPg0fHx/s3r0b+/btA5BXl5MmTcLQoUNLVI7CvHjxQji3f63shb1ZlN/nDxuLe60q7rXne66Txfk7YYwxxn4XHDBnjDHGGACgcePGiI2NxZEjR9C0adMi7SMJML169arQ9ZLlX5vc7FvU1dW/+Kr4lz73R1FTU/tXv9uSJUuQkpKCjRs3onv37lLrBg8ejGPHjpX42BLy8vLo2bMnFi9ejK1bt2LAgAEIDQ2FrKysMBkqkDf69OLFi+jfvz/WrFkjdYzw8HCEhYUV6fMkE0bm5OQUWFfYwwVJ/aWlpaF8+fJF/VoF/Bd1WRgZGRkYGhoiLCwMjx49wsaNG9GhQwdhwsKvUVFRwdy5czF37lw8ePAAR48exerVq7Fs2TJkZGQgMDBQavt58+Zh165dWLx4MeTk5DB//vwil1NDQwPy8vJCwC8/OTk52Nra4uTJkzh+/DhcXFyKfFwAsLS0RIMGDXDixAkkJiZCUVERMTExqFWrllRqoh/xO9LS0oKXlxdSU1MxZ84cTJ06FUuXLi10W8l3LUoAWktLC2/evIFYLBZGeFesWLHQdC6fL9u1axeqVKkCIyOjb34OkJcqKTg4uMByyYjl/COTv0by0CR/SpYvmTRpEnJzc/H3338DyBshXalSJakgq5WVVaHl+tzX2lJRSP7mx40bh4ULFxZpH319fYSFhUEsFuPq1auIjo6Gv78/hg0bBk1NTWHi4n+bpOy7d+9GmzZtirxfcUfCF/fa8z3XyeL8nTDGGGO/C85hzhhjjDEAQO/evSErK4ugoCAkJSV9dVvJKNDatWtDUVER58+fLzRoIwmAWVpalrhc9erVQ3p6Oi5evFhgXVxcXJGPIwmC5R/h/S3169dHRkYGzp07V2Ddj/hu9+7dA5A3Cjy/3Nzcr468LC7JKN+QkBAcO3YM9+7dg6urK6pXr/7NsgDFq2dJmobP8zwDeWkGPtewYUMA/0t1UFLFrcuStIevEYlEWLZsGUQiESZNmlTs4xoYGKBv3744duwYVFVV8c8//xTYRlFREVFRUWjZsiX8/PwwYcKEYn2Gubk5Hj58iOzs7ALrJG1k3rx5IKKvHqewUeCS/UNDQxESEgIikhpdDvzY9j558mRUq1YNK1euxMOHDwvdRpJT3MLC4pvHq1u3LnJycqTmW3ByckJERIRQbiAvpUxkZKTwc2xsLNauXYuxY8d+V3oQsViM8PBwyMnJoUOHDkXa5+zZswDygsnf2m7VqlVYsWKF1Nsvn/8eMzMzi/wdvtaWvsXGxgYikQinT58u9r6ysrKwtLTEhAkThLkN8v+t/Oi/689JzlclKXtxFPfa8z3Xydu3b6NatWoF5pBgjDHGfmccMGeMMcYYAMDIyAgTJkzAmzdv0LJlSzx48KDANpmZmVi8eLEwErJcuXLo1q0b3rx5A19fX6ltDx8+jP3798PIyEhqlGlxSSYsmzJlilQQ5OrVq0VOfQH8L09rUSYAlJBM1Dlp0iSpwNCzZ8+EUb6fj5QtDkmu5hMnTkgtnz9/Pq5du1bi437OzMwMjRo1wunTp4XUH58HM79UlmPHjhUYcf41ktGxkknrJHbs2FHoCOKhQ4dCTk4OI0aMwJMnTwqsf/fuXaGB9s8Vty5L0h6+xdLSEu3atcOtW7ewefPmr26blJRUaDAsJSUFWVlZhU5wCOTlDI+MjETr1q2xYMECjB8/vsjla9KkCTIzM3H16tUC63r27AkHBwfExsaiT58+eP/+fYFtXr16hQEDBgjpUfL7888/oaioiPXr1yM0NBRycnJC3meJH9nelZSUMHHiRGRnZ2P27NmFbnP27FlUrVpVKgf5l1hbW6Ny5cpSI6ynTp0KIoKNjQ0GDx6MIUOGwMbGBtWqVQMAdOjQAa6urmjTpg3GjRtXpHJnZ2cXSGWSm5sLLy8v3L59GyNGjBCOD+QF6F+8eFHgODdv3hT+lrt27frVzxswYAA8PDzQvn17YbmpqSnS0tJw/PhxAHlvhBw4cACmpqZF+h5fa0vfUqVKFXTu3BmnTp3CggULCn1Ac/bsWeEh7LVr1wqdDFYycjr/38q/8Xedn4eHB/T09LB48WKh7vLLzs4u0L5LorjXnpJeJx8/foyXL19KzWXBGGOMMQDEGGOMMfb/xGIxjRkzhgCQvLw8ubq60rhx48jb25u6dOlCFStWJAA0Z84cYZ/Xr1+ToaEhAaCmTZvSpEmTqFu3biQvL0/KysoUFxcn9Rm9evUiAPTgwYMCn+/j40MA6OjRo1Jlat68OQEgCwsLmjBhAvXv359UVFSoTZs2BIB8fHykjlOjRg2qUaOG1LLr16+TSCSi6tWrk7e3N/n6+tLKlSuF9U2aNKHPu0a5ubnk4eFBAKh27drk5eVFQ4YMEeph0aJFUtuHhIQQAAoJCSnw3Y4ePVqgrBcvXiR5eXlSVFSkXr160dixY+mPP/4gRUVFat26dYG6ePDgAQGgXr16FTj+t6xZs4YAEADS0tKiT58+Sa1///496evrEwBq1aoVTZgwgTw8PEhWVpY6dOhQ5HpOT08nAwMDAkDNmjUjLy8vat26NSkpKVGrVq0KfCcioqCgIJKVlSUlJSXq0KEDTZgwgQYPHkwtWrQgBQUFGjRo0De/X3HrMicnh6pVq0aKioo0dOhQmjdvHvn6+tK7d++++jmS37Gvr2+h6y9fvkwikYiMjIwoOzv7i3V16dIlAkB16tShnj17kre3Nw0YMIAqV65MAGjVqlUFPjN/u8rKyhLa/9ixY79ZP0REsbGxBIDmz59f6Prk5GRq2rQpASBNTU3q0qULeXt709ixY8nNzY0UFRVJVlaWYmNjC92/e/fuQhtr165dgfUlbe+urq6Ffl5GRgZVq1aN5OTk6O7du1Lr7t69SwBoyJAhRaobIqL58+eTjIyM1DkrLi6ObGxsqFy5cqSjo0MzZ86ksLAw0tDQIGdnZ1q/fj3l5uYW+TOePHlCampq1LFjRxo/fjyNGjWKateuTQCodevWlJmZKbV9SEgIycnJkYuLCw0ePJjGjx9P7dq1I3l5eQJA06dP/+rnzZ07l9TU1OjZs2dSyz98+EDa2tpUqVIlGjVqFNnZ2REAioyMLNL3+FZbkpCc9z4/ZyUnJ5OlpaVwXh84cCCNHz+eunbtSsbGxgSAXrx4QURES5YsIRkZGXJwcKABAwaQt7c3de7cmRQVFUlJSYni4+OF4+7bt48AUK1atWjq1Knk6+tLmzZt+ub3kZz/JZ/5NefOnROuAc2aNaNRo0bRqFGjqH379lSpUiWqVauW1PYAqEmTJt/87PyKe+0pyXWSiCg4OJgA0NatW7/5vRljjLHfCQfMGWOMMVbA+fPnqW/fvmRkZERKSkqkoKBA+vr61K1bN4qOji6wfVJSEo0cOZJq1KhB8vLyVKlSJerYsSNdvXq1wLbFDZgTEX38+JEmTJhA1atXJwUFBTIzM6PAwMBCg9BEhQdyiYhCQ0PJwsKCFBQUCIDUNoUFLYiIsrOzaeHChcJ+5cuXpyZNmtCuXbsKbFvcgLlkeePGjal8+fKkoaFBrVq1ovj4+ELr4nsC5mlpaaSiovLVAOv9+/epQ4cOpKWlRcrKymRjY0Ph4eHFruf79++Th4cHlS9fnlRUVKhZs2Z0/vz5L/5+ifKCUF27dqVq1aoJbahBgwbk7e1NN2/eLNJ3LE5dEhGdOXOGmjRpQuXLlxcCvYW1y/y+FTAnIuEBw9q1a4Vln9dVSkoKzZgxgxwdHalq1apUrlw5qlatGrm5udHBgwcL/czP21VWVha5u7sTABozZsxXyy1Ru3ZtsrCw+OL63Nxc2rFjB7Vr146qVatG5cqVI2VlZTI3N6eRI0fSjRs3vrhvTEyMUI///PNPoduUpL1/KWBORLR8+XICQD179pRaPmPGDAJACQkJX9z3cxkZGWRpaUmVK1emK1euFHm/4khLS6OePXuSoaEhKSoqUvny5cnOzo7WrFlDYrG4wPaXL1+mnj17kqmpKamrq5OcnBxpa2tT27ZtC7STz929e5cUFRWlHgzmFx8fT3/88YfwMGDZsmXF+i7faktEXw6YE+U9XPPz8yMrKytSUVEhJSUlMjAwoHbt2tH69euFB043btygUaNGUf369alixYqkoKBAhoaG1Lt370Lbo5+fHxkbGwsPFb4WrJYoTsCciOjp06c0atQoMjY2JgUFBVJTUyNTU1Pq378/HTlyRGrbkgTMiYp37SEq/nWSiMjJyYkqV65MWVlZRfrejDHG2O9CRPSNJIWMMcYYY4yxX0JQUBAGDRqEM2fOCPmYfzU5OTkwMTGBvr4+YmJiirXv48eP4ezsjNevX2Pu3LkYPHgwypUrJ7UNEeHw4cPw9/fHqlWroKOj8yOLX2b8Dm3pV3b37l3UqlULPj4+mD59emkXhzHGGPupcMCcMcYYY4yx34RYLIaFhQX09fWxb9++0i7OvyIkJAT9+vXD+fPnYWVlVez9k5OT0bdvX/zzzz/Q0NCAs7MzDAwMUK5cOTx58gRxcXF4/Pgx2rZti8DAQFSpUuVf+BY/v9+hLf3KevXqhUOHDuHOnTtQUVEp7eIwxhhjPxW50i4AY4wxxhhj7L8hKyuLkJAQHDhwAB8+fICqqmppF+mHE4lEWLNmTYmC5QBQsWJF7Nq1CydOnMDGjRsRGxuLQ4cOQSwWQ1dXF66urujbty8aNWr0g0tetvwObelXlZOTA2NjY/z1118cLGeMMcYKwSPMGWOMMcYYY4wxxhhjjDEAMqVdAMYYY4wxxhhjjDHGGGPsZ8ABc8YYY4wxxhhjjDHGGGMMHDBnjDHGGGOMMcYYY4wxxgBwwJwxxhhjjDHGGGOMMcYYA8ABc8YYY4wxxhhjjDHGGGMMAAfMGWOMMcYYY4wxxhhjjDEAHDBnjDHGGGOMMcYYY4wxxgBwwJwxxhhjjDHGGGOMMcYYA8ABc8YYY4wxxhhjjDHGGGMMAAfMGWOMMcYYY4wxxhhjjDEAHDBnjDHGGGOMMcYYY4wxxgBwwJwxxhhjjDHGGGOMMcYYA8ABc8YYY4wxxhhjjDHGGGMMAAfMGWOMMcYYY4wxxhhjjDEAHDBnjDHGGGOMMcYYY4wxxgBwwJwxxhhjjDHGGGOMMcYYA8ABc8YYY4wxxhhjjDHGGGMMAAfMGWOMMcYYY4wxxhhjjDEAHDBnjDHGGGOMMcYYY4wxxgCU0YD5ypUrYWBgAEVFRVhZWSEuLu6r2x87dgxWVlZQVFSEoaEhVq9e/R+VlDHGGGOMMcYYY4wxxlhZUeYC5lu3bsXo0aMxZcoUXLp0CQ4ODmjZsiUeP35c6PYPHjxAq1at4ODggEuXLmHy5MkYOXIkdu7c+R+XnDHGGGOMMcYYY4wxxtjPTEREVNqFKI6GDRuiQYMGWLVqlbDM1NQU7dq1g6+vb4HtJ06ciH/++Qc3b94Ulg0ePBiXL1/G6dOn/5MyM8YYY4wxxhhjjDHGGPv5lakR5p8+fUJ8fDxatGghtbxFixY4depUofucPn26wPaurq64cOECsrOz/7WyMsYYY4wxxhhjjDHGGCtb5Eq7AMXx5s0biMViaGtrSy3X1tbGy5cvC93n5cuXhW6fk5ODN2/eoGrVqgX2ycrKQlZWlvBzbm4u3r59i4oVK0IkEv2Ab8IYY4wxxhhjjDHGGGPsv0BEeP/+PapVqwYZma+PIS9TAXOJz4PWRPTVQHZh2xe2XMLX1xczZ878zlIyxhhjjDHGGGOMMcYY+1k8efIEOjo6X92mTAXMK1WqBFlZ2QKjyV+/fv1/7N17eFTVvf/x956ZJNwTIYRIQRHqEaqiIHi/thY9arVabb2UarGotfQgVrzU1qKtUsF66dFaamtt1dLf6fFSLS3V9mi19QIIkaKAllAUMXKTBEhIMjP798eeGSZhJpmQme/Mnnxez8OjWZnLWp+95ps9a/bsvcdR5HHV1dUpbx8KhRg0aFDK+9x0001ce+21iZ/r6+vZb7/9WLt2LQMGDAAgEAgQCASIRqNEo9HEbePtkUiE5NPDp2sPBoM4jkM4HG7Th2AwCEAkEsmoPRQK4bpum3bHcQgGg3v0MV17R2O6f599cEIhSPqQwQ2HwXXTt5eUtOmjGzsFTpfaHcd7/ESjixsO45SVUTV9OpseeAC3pSXRTiCAE8sIwI1GIRKBYBAn6dMjNxKBaDSrY/p60jzr7nZ6sLq6IMaU6GNpKYO//nU2/vjH0O6yBx1tp7TtWRrTN7Zuzcrr6f7BgwtmTE4ohFNWxuBvfINNDzxAtLEx56+nTMf0jY0bs1L3flJZWTBjcqNRnECAqmuuYdNPfuLVE3L7esp0TPGako1a/mB1dUGMCWL15Oqr2XjvvV49z/HrKdMxfXPbtqz9zX2gqqogxgTe9nBKS3fXlJ07E+173N647sVrSnf3jX4S258rhDHF5/QeNcVg36izMcVrSnf3YR+sri6YMUG7mhKr6Ymb57Huta8pe1vLfzxwYMGMCcch0KdPop64zc1m+0adjenrdXVZe//0wMCBBTGmeO5OSQlVM2bsft9D59sp13Xv6x99lGjv7vvcRE3J85ji2yPxvue++8B1zd/npmuf9vHHGb9376iW//egQQUzJgIBAr17795H2bXL/H1uuvav19VlZY3lJ1VVBTOmuKrp09n04IOJemL5PjfdmDJZS8m0lrdZS8lzLY/vg2+8917clhbz97npxvRfH39cdGuWma5HNDQ0cMABB9C/f38646sF89LSUo444gief/55zj333ET7888/zznnnJPyPscccwzPPvtsm7bnnnuOCRMmUJKieACUlZVRVla2R/vAgQMTC+Y9TS+Adi+QhHTt6c4R35V2103Z7rgufUpKKGttTRQZAKJR7197kYj3r70sjmngwIGpf7cXygpkTHGO49CnrIxe0DbvuDTbKW17lsa0zz77pO57F5UV0JgIh3ECgd3zO17wc/h6ynRMFRUVqZ+ri3oV0JgAnGCQPqWle9YTyGvdy1lNyXMtdxyHPqWl9HIcb37n+PWUaR/Ly8tT33YvpKwpeazljuPsrikd3d647mWtpsT/pwDGBLvneMqakse6l62assf8znMtb1NTDPeNOmvPVk3pVUBjwnVxWlt315P4cxVALc/m38xeUBBjimtTwzN5/RnUvZzto0Dea3nifY/jpH7fk66PXW3v4piy9TezLA/v3TvaTm1qSnzbFEDdy9Yc71VAYwJvAbdPWVnqepLHupezmpLnWh6v373A2wc3fp8LpBxTT13XBG/hHsjodNu+uugnwLXXXsvPf/5zHn74YVauXMmMGTN47733uOqqqwDv6PCvfOUridtfddVVrFu3jmuvvZaVK1fy8MMP84tf/ILrrrsuX0OQLHCjURprarxPzSTnlLct5W1LedtS3vaUuS3lbUt521Le9pS5LeVtS3nbUt62lLe/+eoIc4AvfelLbNmyhdtuu40PP/yQQw45hD/+8Y/sv//+AHz44Ye89957idsfcMAB/PGPf2TGjBk88MADDB06lB//+Md84QtfyNcQJBvCYeoXLMh3L3oO5W1LedtS3raUtz1lbkt521LetpS3PWVuS3nbUt62lLct5e1rvjvCHODqq6/m3//+N83NzbzxxhuceOKJid898sgjvPjii21uf9JJJ7F06VKam5tZu3Zt4mh08bFQiPIzz4SQ7z7z8SflbUt521LetpS3PWVuS3nbUt62lLc9ZW5LedtS3raUty3l7Wu+XDAXcQIB+hx+eJuLF0juKG9bytuW8ralvO0pc1vK25bytqW87SlzW8rblvK2pbxtKW9/01YTEREREREREREREUEL5iIiIiIiIiIiIiIigBbMxafcSIQdL7+MG4nkuys9gvK2pbxtKW9bytueMrelvG0pb1vK254yt6W8bSlvW8rblvL2N515XvwpEmH7Sy/luxc9h/K2pbxtKW9bytueMrelvG0pb1vK254yt6W8bSlvW8rblvL2NR1hLr7klJQw8KKLcEpK8t2VHkF521LetpS3LeVtT5nbUt62lLct5W1PmdtS3raUty3lbUt5+5sWzMWfHIeykSPBcfLdk55BedtS3raUty3lbU+Z21LetpS3LeVtT5nbUt62lLct5W1LefuaFsxFRERERERERERERNCCuYiIiIiIiIiIiIgIoAVz8Sk3HKZ+wQLccDjfXekRlLct5W1LedtS3vaUuS3lbUt521Le9pS5LeVtS3nbUt62lLe/hfLdAZG9Eo3SWFOT7170HMrblvK2pbxtKW97ytyW8ralvG0pb3vK3JbytqW8bSlvW8rb13SEufiSU1LC4Cuv1NWGjShvW8rblvK2pbztKXNbytuW8ralvO0pc1vK25bytqW8bSlvf9OCufiT4xCqrNTVhq0ob1vK25bytqW87SlzW8rblvK2pbztKXNbytuW8ralvG0pb1/TgrmIiIiIiIiIiIiICFowFxEREREREREREREBtGAuPuWGw2ydP19XGzaivG0pb1vK25bytqfMbSlvW8rblvK2p8xtKW9bytuW8ralvP0tlO8OiOyVaJTm2tp896LnUN62lLct5W1LedtT5raUty3lbUt521PmtpS3LeVtS3nbUt6+piPMxZec0lKqZ87EKS3Nd1d6BOVtS3nbUt62lLc9ZW5LedtS3raUtz1lbkt521LetpS3LeXtb1owF99S0bGlvG0pb1vK25bytqfMbSlvW8rblvK2p8xtKW9bytuW8ralvP1LC+YiIiIiIiIiIiIiImjBXEREREREREREREQE0IK5+JTb2sqmefNwW1vz3ZUeQXnbUt62lLct5W1PmdtS3raUty3lbU+Z21LetpS3LeVtS3n7mxbMxZ9cl0hDA7huvnvSMyhvW8rblvK2pbztKXNbytuW8ralvO0pc1vK25bytqW8bSlvX9OCufiSrjZsS3nbUt62lLct5W1PmdtS3raUty3lbU+Z21LetpS3LeVtS3n7mxbMRURERERERERERETQgrmIiIiIiIiIiIiICKAFcxERERERERERERERQAvm4lNuSwt1c+fitrTkuys9gvK2pbxtKW9bytueMrelvG0pb1vK254yt6W8bSlvW8rblvL2Ny2Yiz85DsEBA8Bx8t2TnkF521LetpS3LeVtT5nbUt62lLct5W1PmdtS3raUty3lbUt5+5oWzMWXnJISBl95JU5JSb670iMob1vK25bytqW87SlzW8rblvK2pbztKXNbytuW8ralvG0pb3/TgrmIiIiIiIiIiIiICD5bMP/444+ZPHky5eXllJeXM3nyZLZt25b29q2trdxwww0ceuih9O3bl6FDh/KVr3yFDRs22HVaRERERERERERERHzBVwvmF198MTU1NSxcuJCFCxdSU1PD5MmT096+sbGRpUuX8t3vfpelS5fy5JNP8s4773D22Wcb9lpyRRdOsKW8bSlvW8rblvK2p8xtKW9bytuW8ranzG0pb1vK25bytqW8/ctxXdfNdycysXLlSj71qU/x2muvcdRRRwHw2muvccwxx7Bq1SoOOuigjB5n8eLFHHnkkaxbt4799tsvo/s0NDRQXl5OfX09AwYM2Osx+NldukhBp67L4ktJeWcmW5kr78wob1uqKbaUtz3VFFvK25bytqUabkt521NNsaW8bamm2Mpm3n7TlfXdkFGfuu3VV1+lvLw8sVgOcPTRR1NeXs4rr7yS8YJ5fX09juNQUVGR9jbNzc00Nzcnfm5oaAAgHA4TDocBCAQCBAIBotEo0Wg0cdt4eyQSIfmziHTtwWAQx3ESj5vcDhCJRDJqD4VCuK7bpt1xHILB4B59TNfe0ZjAu2BB8tV93XAYotH07aWlbfrotraC6+7Z3tICjrPHhRDclhYIBHBCSdPUdb3HCYXoNWoUzevWQTS6uz0YxIllBOBGoxAOQyiEE9j9hQo3EoFIJKtjSt6G3d1OTmlpQYwpIRCgdOhQmteta9MX6GQ7pWvP0piy9XpKOSfzNCanpASCQcr235/mdeu8vuT69ZThmKLRaFbqHoFAwYzJjUYhGqVs1Cha3n/fqyfk+PWU4Zji8zgbtdwpLS2IMcU6SOl++9H8r395fcv16ynDMbWvzbD3tbzN4xdCLQ8EdteUXbty+3rqwpjiNaW7tRwomDG5sT6XffKTtLz33u6aYrFv1MmYsrUPG+9rIYwp1kFKhw+nec0a7/ZW+0adjClb++Wm+3udjIlAAKe0NFFPiETs9o06GVM4HM7a+yegIMYEse3kOPQ68MDd73sw2DfqZEzJ9bm773MTNSXPYyJp25cOG0Zzba33GNbvc9O0d+W9e0e1PC/v3TvaTiUlu/dRwmH797lpxhQOh7OyxkIoVDBjij9O2QEH0PLBB4l6Yvo+N82YMllLybSWt1lLyXctj+2D7/rXv7zHsX6fm25MsRyLac0y0/WI9uPoiG8WzOvq6qiqqtqjvaqqirq6uoweY9euXdx4441cfPHFHX6SMHv2bG699dY92pctW0bfvn0BGDx4MKNGjWLt2rVs2rQpcZthw4YxbNgw3nnnHerr6xPtI0eOpKqqihUrVtDU1JRoHz16NBUVFSxbtqzNxBk7diylpaUsWbKkTR8mTJhAS0sLy5cvT7QFg0EmTpxIfX09q1atSrT37t2bww47jM2bN1NbW5toLy8vZ8yYMWzYsIH169cn2jsaE8A+559P2ciRifb6BQtorKmhcsoUQpWVifat8+fTXFvLkOnT2xSETfPmEWlooHrmzDZjqps7l+CAAQy+8spEm9vSQt3cuZSNGMHAiy5KtIc3b2bTvHn0OfxwhkybRvOaNbjRKM21tWydP5/+xx1HvxNOSNy+saaG+gULKD/tNPocfniifcfLL7P9pZeyOqbkbdXd7VQ9c2ZBjCnOCQRwgc0//zmVU6Zkvp3GjqX8zDMT7dkeU7ZeT8ljzfeYKqdMoaSqirJRo2hes4Ytjz+e+9dThmNau3ZtVupe2YgRBTOmxpoaGp5/niHTphHevNnbschwO+W67sXnazZqefXMmQUxJvDqSenw4aybNo3en/pUzl9PGY8pEsna39zkbAqhljuBQKKmfHjnnTl9PXVlTJs3b87KvpFTWlowY6pfsICmt99m3+uuo+X99xM1xWTfqJMxxXPr7j5svK+FMCbwakqospL3Z85kwGc/a7Zv1NmYsrVfbrm/19mYykaMYNAllyTqSevGjWb7Rp2NacmSJVl7/wQUxJji2ym6axf73nRT4n1PJtsp13UvOePuvs+Nz8F8jym+nZxAgEC/fnzw3e9S+dWv2r/PTTOmrrx376iW5+W9ewfbqf9JJyVqys6lS83f56Yb07Jly7KyxlJ+2mkFMya3pYWP7ruPyilTcHftStQTy/e56caUXFO6W8urZ84siDE5paWJffD1N91EeMsW8/e56cYEFN2aZabrETt37iRTeT8ly6xZs1IuTidbvHgxzz33HL/61a9YvXp1m98deOCBXH755dx4440dPkZraysXXHAB7733Hi+++GKHC+apjjAfPnw4W7ZsSdyvpx1hfnfs099COcLc6dWL6pkz+eiee7zbFcARfNOTXnjd3U739e1bEGNK9LG0lCHTp1N3113QrmTk82jsGeFwVl5Pd/fqVTBjckpKcMrKGDJjBh/dcw/RxsbCOIIPmNHUlJW6d29ZWcGMyY1GcQIBqq+/no/uvTdxjrm8H8EXCiVqSjZq+X19+xbEmCBWT665hro5c3DD4YI5gu9bkUjW/ubeE1twyfeY4tvJKS3dXVN27CiMI/jYXVO6u290b+xxC2FM8Tm9R03J9xF8wWCipnR3H/a+2MEjhTAmaFdTYjU9cfM81r1vRaNZ2S+/q6SkYMZEIECgT59EPXGbmwvjCD5g+s6dWXv/dE8oVBBjAm87OSUlVN9ww+73PeTu9ZTpmKY3Nibau/s+N1FTCuQI88T7nrlzvfc9BXI09rWRSFaOtPxRaWnBjIlgkEDv3rv3UXbtKpijsafv3JmVNZZ7e/cumDHFVc+cyUf33bf73NoFcDR2JmspmdbyNmspea7l8X3wujlzcJubC+YI8+vC4aJbs8x0PaKhoYFBgwb545Qs06ZN48ILL+zwNiNGjGD58uV89NFHe/xu06ZNDBkypMP7t7a28sUvfpG1a9fyf//3f52GUlZWRllZ2R7toVCIUKhtZImvS7YTTJqombS3f9y9aXccJ2V7uj52td1tbU3Zl7TtaS5ukLLddVO3R6Np2+O/a/P7SCTxFZM2wmFSfTKUzTGlyn5vt1OhjGnPxq5vp5TtWRpTtl5PhTSm+FdvE88TTfrqbaZ9z9F2is/Pbte9AhoTAPGv7bWvJ+T39ZTp35tM2pOfpyBqRHxHxuL1lKo9xXOmq83Q9VpeKGNq056cdYHU8vj8zEotL5Axte/PHjUlj3UvW/uwhTSm5McFCmrfKFv75YU0puR9E7elZfdzFUAtT846G7W8EMaUqj/t/5bmq+6lymxva3mhjCmVQnr/1NXakW57FNKYiETa1BTiC3IFsJ3i87PbtbyAxgRtT4G0x33y+HrKdC0lXXvydmozhkKoEdFoYj+lkNZYinHNMpMxpetvKnlfMK+srKQy6SsM6RxzzDHU19ezaNEijjzySABef/116uvrOfbYY9PeL75Y/u677/LCCy8waNCgrPVd8sh1CW/evPsNkuSW8ralvG0pb1vK254yt6W8bSlvW8rbnjK3pbxtKW9bytuW8va1FIcxFKYxY8Zw+umnM3XqVF577TVee+01pk6dyllnndXmgp+jR4/mqaeeAryTuZ8fO8fa448/TiQSoa6ujrq6OlrSfXovvuC2trJp3rz0R3ZJVilvW8rblvK2pbztKXNbytuW8ralvO0pc1vK25bytqW8bSlvf/PNgjnA448/zqGHHsqkSZOYNGkSY8eO5dFHH21zm9WrVydO8r5+/XqeeeYZ1q9fz+GHH86+++6b+PfKK6/kYwiSLYGAd+GCVF9dlexT3raUty3lbUt521PmtpS3LeVtS3nbU+a2lLct5W1LedtS3r7mq602cOBAHnvsMRoaGmhoaOCxxx6joqKizW1c1+Wyyy4DvHOfu66b8t/JJ59s3n/JHicUovzMM9te7EByRnnbUt62lLct5W1PmdtS3raUty3lbU+Z21LetpS3LeVtS3n7m68WzEVEREREREREREREckUL5iIiIiIiIiIiIiIiaMFc/Mp1aa6t1dWGrShvW8rblvK2pbztKXNbytuW8ralvO0pc1vK25bytqW8bSlvX9OJdMSX3NZWts6fn+9u9BjK25bytqW8bSlve8rclvK2pbxtKW97ytyW8ralvG0pb1vK2990hLn4UzBI/xNPhGAw3z3pGZS3LeVtS3nbUt72lLkt5W1LedtS3vaUuS3lbUt521LetpS3r2nBXHzJCQbpd8IJOCo8JpS3LeVtS3nbUt72lLkt5W1LedtS3vaUuS3lbUt521LetpS3v2nBXEREREREREREREQELZiLiIiIiIiIiIiIiABaMBefcqNRGmtqcKPRfHelR1DetpS3LeVtS3nbU+a2lLct5W1LedtT5raUty3lbUt521Le/hbKdwdE9ko4TP2CBfnuRc+hvG0pb1vK25bytqfMbSlvW8rblvK2p8xtKW9bytuW8ralvH1NR5iLP4VClJ95JoT0mY8J5W1LedtS3raUtz1lbkt521LetpS3PWVuS3nbUt62lLct5e1rWjAXX3ICAfocfjhOQFPYgvK2pbxtKW9bytueMrelvG0pb1vK254yt6W8bSlvW8rblvL2N201ERERERERERERERG0YC4iIiIiIiIiIiIiAmjBXHzKjUTY8fLLuJFIvrvSIyhvW8rblvK2pbztKXNbytuW8ralvO0pc1vK25bytqW8bSlvf9OZ58WfIhG2v/RSvnvRcyhvW8rblvK2pbztKXNbytuW8ralvO0pc1vK25bytqW8bSlvX9MR5uJLTkkJAy+6CKekJN9d6RGUty3lbUt521Le9pS5LeVtS3nbUt72lLkt5W1LedtS3raUt7/pCHPJuQjwV2AJ0AAMBI4Fjs/gvh8AC4E6YAdQAgwGjotGOXPkSHCcxG1d4B/AK8BWoA9wMPCfsf+PawWeBZbjvQCOAk4FnKTbfAzcBVwGHNiFsRYtx6GsXd6SQ8rblvK2pbztKXNbytuW8ralvO0pc1vK25bytqW8bSlvX9OCueTck8BS4DRgOLAaeAZoBj7TyX13ARXA4UA50AIsA+ZHIrT+/e8cnXTbPwAvAyfhLXJ/BDwHrAemAcHY7V4EVgDnxR7/abxF/CPa9flQtFguIiIiIiIiIiLSk2jBXHKqDlgMnA6cHGsbBTTiHXV+DG2P/m5vVOxfsk8BWx2HF5YtSyyY1wN/xzty/cxY238A/YDf4B3dflSsfSXe0e1jYz+/F2uLL5jXxNquz2yIIiIiIiIiIiIiUiR0DnPJqbfwTpUyoV37BLxTo6zey8ft67qwaxduOAzAOiAKjG53uzGx/y5PagsDpUk/l8baAJqA3wOfA/ruZd+KkRsOU79gQSJvyS3lbUt521Le9pS5LeVtS3nbUt72lLkt5W1LedtS3raUt7/pCHPJqTq8hecB7dqHJv0+E1G8hfcmvMXv1cDnW1oSv4/E/tt+Qgfxzk2e/Dz7A4vwzm/eDLzJ7vOpLwCq2XOBv8eLRmmsqcl3L3oO5W1LedtS3vaUuS3lbUt521Le9pS5LeVtS3nbUt62lLev6QhzyamdpD7lSineYvbODB/nKeBG4Fa8859/PhDg7CuvTFxteEjsdv9ud791eAvtyc8zCW9h/Q7gR3iL98cDtXjnWj8vwz71JE5JCYOT8pbcUt62lLct5W1PmdtS3raUty3lbU+Z21LetpS3LeVtS3n7m44wl6yJtPs5/mlMR9cDzvRawZ8GjgR2AG8DT0WjlP3rXxwRu9rwUGAk8DdgMN75yz8Cnoj1I/l5+gPfBD7GW7QvxzslyxN4FyEdjHcU+3NAAzACbxG9IsO+FiXHIVRZqas7W1HetpS3LeVtT5nbUt62lLct5W1PmdtS3raUty3lbUt5+5oWzCUrtgKz27VdhXc6lg0pbt+Ct8De0QU/k+0T+wfeecmdQID/eeEFDgoGE+canwz8P+Cx2M9B4ETgXbxTuSRzgIFJP78QazsZ2AjMBy7FW4R/Mvbz1zPsq4iIiIiIiIiIiPiTFswlKwYA/9WubTDe+cBr8I7UTj6P+Yex/1bv5fMNdxxejUTYEggkFsz7AZfjHYXegLfAXgK8Aozt4LE24i2YX4G3yP4u3ile4hcQPRG4B+9852V72V8REREREREREREpfDqHuWRFCBje7l8vvAtrOsAb7W6/BG8x+6C9fL5/RSLeUeKR9ieC8RbOhwK9gdfwjmY/toPHegLvIp8jYj+7sfvEtSS191RuOMzW+fN1dWcjytuW8ralvO0pc1vK25bytqW87SlzW8rblvK2pbxtKW9/0xHmklPVwES884E7eAvp7wCvA6fR9pQszwN/wTvSe1Ss7X/xjureD28hfCfe+cXfBE4C+rm7l7Ffj/13EN4pWFYBi4HTgWFp+rcI2AxcltR2IPAs8GfggFjfR+B9ANBjRaM019bmuxc9h/K2pbxtKW97ytyW8ralvG0pb3vK3JbytqW8bSlvW8rb13SEueTcecApwD+Ah4B/AmfjXWAzmQtE27XtD7wPPAX8DG8BfTtwUTDI12bOxCktbXP/l4CH8c5lvgn4Ct4FQ1PZASwAzsE7Gj1uCHAh3qlkfg2UAhdlONZi5ZSWUt0ub8kd5W1LedtS3vaUuS3lbUt521Le9pS5LeWdXgTvYLI7gBuBOcDf9/KxXgdmAjdDh3m7wE9it32q3e9a8a43Ngv4Ad4BeO2/Ff5x7Dne3ct+FhvNb1vK2990hLnkXBCYFPvXkVS3mRj7154TDO5RdI6O/ctUP+DWNL8bF/snu6nI21LetpS3LeVtT5nbUt62lLct5W1PmdtS3qk9CSzF+6b4cGA18Azetb7aHwzXkXrgD3jXONtFx3m/AmxJ87sXgRV4B+jtAp4GBgJHtOvzoXjfIheP5rct5e1fvjrC/OOPP2by5MmUl5dTXl7O5MmT2bZtW8b3v/LKK3Ech3vvvTdnfRQRERERERERKRZ1eKc7/SxwMt4pVM8AjgT+CjR24bGeAEbS+SL2VuBPwOfT/H4lcDwwNtaP8bG2uBrgPeBzXeibiEicrxbML774Ympqali4cCELFy6kpqaGyZMnZ3Tfp59+mtdff52hQ4fmuJciIiIiIiIiIsXhLbzTnUxo1z4B79QoqzN8nDeAWuDcDG77v3iL6oem+X0Y7/SpcaWxNvCuafZ7vMXyvhn2TUQkmW9OybJy5UoWLlzIa6+9xlFHHQXAQw89xDHHHMPq1as56KCD0t73gw8+YNq0afz5z3/mzDPPtOpyUbnObX82sPxyXZempiZ6f//7OI6T7+4UPbe1lU3z5uG2tua7Kz2C8ralvG0pb3vK3JbytqW8bSlve8rclvJOrQ5v4XlAu/ahSb/vzA68U7icAVQktafK+3W8a5ld18Hj7Q8sAg7GOy3Mm3hHnIN3rbJq9lzg7+k0v20pb3/zzYL5q6++Snl5eWKxHODoo4+mvLycV155Je2CeTQaZfLkycycOZODDz44o+dqbm6mubk58XNDQwMA4XCYcNj7zDIQCBAIBIhGo0Sjuy9VGW+PRCK4SYvM6dqDwSCO4yQeN7kdIBKJZNQeCoVwXbdNu+M4BIPBPfqYrt1vYwoGg4TDYRzHKZoxxfvolJaC63qFNRjEiT0egBuNQjgMoRBOYPeXRNxIBCIRnJISSPoQwQ2HIRpN397unFpuayu47h7tkdjrYI/bt7RAIIATSion8b6na8/SmLK1nQppTPH26K5dOCUlXd5ObksLOI73OFkeUzQazcrriUCgYMYU306RnTvbPH6uX0+ZjCk+j7NRy53S0oIYU1y0qQlc1+z1lMmY2tdm2Pta3ubxC6SWJ2qKwesp0zHFa0p3azlQMGOKb49oU1PbmlIAtTxb+7DxvhbCmOIiO3d6NcV436ijMWVrv7yQxhTfHvF6Yr1v1NGYwuFw1vbLgYIYE+zeTonM4+15ruXJ9bm7758SNaWA/j5FduzwXh+G+0adjSlb73P3dkyNra30iT9e0pjKgGBLCzu9J+lwTE+2tlIFHBsMetvDcdrM7/h2qg8G+UMkwlnBIBXBYGI7tX/8Sa2t/NJ1uSP282jH4YRQiNpwmKWuy7UlJW0OruvKdgqHw1lZYyEUyst7947mXmT79rbtBVDLk+tEd2t5m7WUAqjl0V27Ev+fj/e5KccUy7EQ18I6as/G+l77cXTENwvmdXV1VFVV7dFeVVVFXV36zzPvvPNOQqEQ//Vf/5Xxc82ePZtbb93zcpDLli2jb1/vCz2DBw9m1KhRrF27lk2bNiVuM2zYMIYNG8Y777xDfX19on3kyJFUVVWxYsUKmpqaEu2jR4+moqKCZcuWtZk4Y8eOpbS0lCVLlrTpw4QJE2hpaWH58uWJtmAwyMSJE6mvr2fVqlWJ9t69e3PYYYexefNmamtrE+3l5eWMGTOGDRs2sH79+kS7n8a0ceNGampqqKiowHGcohhT8naqnjmT5tpats6fT//jjqPfCSckbt9YU0P9ggWUn3YafQ4/PNG+4+WX2f7SS+xz/vmUjRyZaK9fsIDGmhoqp0whVFmZaN86fz7NtbUMmT69TeHeNG8ekYYGqmfOTLQ5gQAusPnnP6dyypREu9vSQt3cuZSNGMHAiy5KtIc3b2bTvHn0GTuW8qRvdWR7TNnaTsljzfeYKqdMoaSqirJRo2hes4Ytjz+e8XYCqJs7l+CAAQy+8sqsj2nt2rVZeT2VjRhRMGNqrKmh4fnnGf7DHxLevNnbschwO+3t6ynTMcXnazZqefXMmQUxJvDqSenw4aybNo3en/pUzl9PGY8pEslaLU/OphBquRMIJGrKh3femdPXU1fGtHnz5qz8zXVKSwtmTPULFtD09tvsd889tLz/fqKm5Or11JUxxXPr7r5RvK+FMCbwakqospL3Z85kwGc/a7Zv1NmYsrVfbrm/19mYykaMYNAllyTqSevGjWb7Rp2NacmSJVnbLwcKYkzx7RTdtYv977+f5jVrEjUl1/tGnY0pOePuvn+Kz8F8jym+nZxAgEC/fnzw3e9S+dWvmuwbZTKmbL3PzWTuRaJRNv3sZ96YvvUtAo5D6fz5lDQ04GzfvseYnB/+ECcS6XBMq0eNYuXTT3P75ZczcPt2ts6fT0lVFc7WrYn5vXPpUuoXLOCZQYMY0asX53z5yziOw46XX4aXXqLXqFFUf/GLicfvvWAB36ypwbn4YnpVVrJP//6EIxHu/dGP+ExrK4dccw2La2t54m9/Y9uOHewfCnEeMDqD7bRs2bKsrLGUn3aa+Xv3dGNyW1r46L77GHrLLbi7diXqieX73HRjSq4p3a3l1TNnFsSYnNLSxD74+ptuIrxli/n73HRjAgp2LQxyu2a5c+dOMuW4bQ77szdr1qyUi9PJFi9ezHPPPcevfvUrVq9ue3asAw88kMsvv5wbb7xxj/u98cYbnHnmmSxdujRx7vIRI0ZwzTXXcM0116R9vlRHmA8fPpwtW7YwYID3JSS/HrncUbufxtTS0sIbb7zB+PHjE8/n9zEl9/G+vn3zfiRLcrtTWsqQ6dOpu+su7wiu5Nvn8Qi+GeFwVrbT3b16FcyYnJISnLIyhsyYwUf33EO0sbFgjuCb0dSUldfTvWVlBTMmNxrFCQSovv56Prr3Xu8xKYwj+KbH/phno5bf17fvXo8pWlLCX6NRFkciNAADgWOBE/ZiTK9HIvwuEqGspITbHQc3HE5sp+ti2acyGLgeIBQi7Dg8G4nwZjRKCDgK+Gy719nW1lbucl2+GgxyYPL27mA7fSsSyVotvye24OI9eP5ruVNaurum7NhRMEfwxWtKd//m3ht73EIYU3xO71FTCuBo7HhN6e6+0X2xg0cKYUwQ20e55hrq5sxJ1PTEzfNYy78VjWZlv/yukpKCGROBAIE+fRL1xG1uLpgj+Kbv3Jm1/fJ7QqGCGBN428kpKaH6hhu8zOM1Jc9HY09v3H2Jx+6+f0rUlAI5wjzxvmfuXO99T4EcYX5tJJKV97k/Ki3tcExbXZc72p0+4qpQiNeiUd6NRpkFbcbU7Lrc3NrKp4H/TNP3ZsfhTtdlfCDAqcGg1x4O86Tj8Lbr8t8zZ7LlJz+hpLmZ5eEwjwPfCIWoivXTjUS4JRrlKMfhrFCIUiAY249s/3p6LhLhzUiEGcDWkhLubm3lslCIkY7DE62tfAxcncF2mr5zZ1bWWO7t3bugjjAHb0H5o/vuS9STQjgae3rSImZ3a3mbtZQ81/L4PnjdnDm4zc0Fc4T5deFwwa6FddSejfW9hoYGBg0aRH19fWJ9N528H2E+bdo0Lrzwwg5vM2LECJYvX85HH320x+82bdrEkCFDUt7v5ZdfZuPGjey3336Jtkgkwre+9S3uvfde/v3vf6e8X1lZGWVlZXu0h0IhQqG2kSW+LtlOMGmiZtLe/nH3pt1xnJTt6frY1fZCG1P8RZT8e7+PKd5HN3nhKBJJfG2mjXCYVJ92pTs/Vtr2NItUKdtdN3V7NNq19iyNKVvbqZDG5La2en90488T+0NQCNspPj+7/XoqoDEBEP/aXkvLHvfJ5+sp0783mbQnP09Xx/REaytLgdOA4XgXdXoGaG5p4TNdGNO2lhaexTv3ZXNSe/z201I893ux5zok3hAO8wLwT+A8YBfwNDAwHOaIpPs9iXeBqE+meV2m6mO62gxdr+U5rxGp2jube8mviQKp5fH5mZVaXiBjat+fPWpKHutetvZhC2lMyY8LFNS+Ubb2ywtpTMn7Jm5Ly+7nstg36qTvyVlno5YXwphS9afN7/NY91Jltre1vFDGlEohvX/K1vvczsbUH2j/3fzB4TDVQA3QAAxIGtOHsdtUd9D3na7LduBv0Sh/S1rkitfuK+fO5WDH4TLXpQ6IAv+d4rQJr7sur7e2cilJ+4ZJY9oIvABcAQSBd1pbGQIcFHusE4F7gF0tLeyx2tNuO8XnZ7dreXwcBTL3kk+BtMd98vh6SpXx3tbyNmMohBoRjSbmeiGtsRTqWlh32jMZU7r+ppL3BfPKykoqk77CkM4xxxxDfX09ixYt4sgjjwTg9ddfp76+nmOPPTblfSZPnsypp57apu20005j8uTJfPWrX+1+50VERIpYHbAYOB04OdY2CmgE/gocA/TJ8LGeAEYCfQIB/pni9/unaHsNcIAjk9pW4l3QaWzs5/dibfEF85pY2/UZ9ktEREREPCG8AyTaOxj4M/AGcEpS+xKgBEh9RTlPf+CqFO3/B9QC13/5yzT/9rcQDjMBb1+zvZ/G+nACscX5FJ7Au8jniNjPLpC8rNiS1C4i0pm8L5hnasyYMZx++ulMnTqVefPmAXDFFVdw1llntbng5+jRo5k9ezbnnnsugwYNYtCgQW0ep6SkhOrq6rQXCRV/CAaDTJgwIe0nSJJdbot3Hq20R71IVilvW8o7vbfw3lRMaNc+AXgd72jzcRk8zht4b4iuAxZGo7jRaKdvVnYBy/EW2ZM/Vg8DyV9mLAV2xP6/Cfg98Dmgbwb96ik0x20pb1vK25bytqfMbSnv1KqBicBzeAczDAfewdsfPI22B1A8D/wF70jvUXgL6qkWwRcDAWDg//yPdyoLvFP/DUzTh/I0jwOwCNgMXJbUdiDwLN5C/wGxvo8AetFzaX7bUt7+luJ7f4Xr8ccf59BDD2XSpElMmjSJsWPH8uijj7a5zerVq9uc5F2KV4uKjh3HIThgQJvzc0kOKW9byjutOryF5/Zndxua9PvO7MA7rcoZQEXyLzrJ+028I4GObNe+P96boo9jz/8mu49OX4D3hq79An+PpzluS3nbUt62lLc9ZW5Lead1Ht7R5f8AHsI7Rd7ZsMcp+ly806pkqrt578DbBzwHSLqKDEOAC/G+ffhrvIMsLmp/555G89uW8vY1Xy2YDxw4kMcee4yGhgYaGhp47LHHqKioaHMb13W57LLL0j7Gv//97w4v+Cn+EIlEWL58+R4XEpDccEpKGHzllSkvGiLZp7xtKe/0dpL6lCuleOeGzOQa40/iXbTzmNjPTiDgXdy2k7wX4b3pObRd+yS8r8fdAfwIb/H+eLwj2JfivZmTtjTHbSlvW8rblvK2p8xtKe/0gnj7YTcDP8Q7/d3xKW43CZhL+qPB4y4E7igtzSjvucC5aX7XD7iV3afrSzYOuAH4Ad4R7+mOXu8pNL9tKW9/880pWURERCR32n/8GP9EvaPjITo7VmI58DYwI4PbJqvDOw/5sXhf403WH/gm3hHmQbyv54bxzlv5GbzF+eV4X7ttwPvq7Xm0O7pdREREREREJA0tmIuIiPRwW4HZ7dquwjsdy4YUt2/BW2Dv6IKfzcBTwHF4p3RpirXHF+abXJcAbc9HHrco9t/2p2OJc2h7hNALsbaTgY3AfOBSvPOfPxn7+esd9FVEREREREQkTgvm4lu64KctXajClvK21dPzHgD8V7u2wXjnA6/BO1I7+TzmH8b+W93BY+7EO6fkS7F/CdEoRKN8FziYthdnAu9o8aXAMOATGfR9I96C+RV4R5y/i3fOytGx358I3IO3gF+WweMVq54+x60pb1vK25bytqfMbRVz3te5nV123VY4HGbZsmV8adYsQiEtT1ko5vldiJS3f6kiiS+FQiEmTpyY7270GPGrO4sN5W1LeXs7A8NTtB8M/Bl4A+8iT3FL8E6VclAHj9kf7yj19v4P73zjX8M7gr29t/EW2yd12mvPE3gX+RwR+9nFOwI+riWpvafSHLelvG0pb1vK254yt6W8bel9vS3Nb1vK2998ddFPkTjXddm2bRtugX1CXrQCAcpGjoSASoYJ5W1LeadVDUzEOx/4i8Aa4E/A63jnC08+JcvzeBdVWhP7uQTvYk/t//UHAo7DqEAg5RHqi2L3HZdB/xYBm4H/TGo7ENiCt9D/DvAHvMX0Xhk8XtHSHLelvG0pb1vK254yt6W8Tel9vTHNb1vK29e01cSXIpEIq1atIhJpf5k6yQUnFGLgRRfh6GtyJpS3LeXdsfPwji7/B/AQ8E/gbLwF82QuEM3g8ZxAACcUSpn3NrxF7rFA704eZwewADin3W2HABfinUrm13jnSL8og34VM81xW8rblvK2pbztKXNbytuW3tfb0vy2pbz9TVtNRERE0grinR6ls1OkZHIbgAtDIa6ZOTPl1xMrgDkZ9qsfcGua340jsyPURURERERERNrTEeYiIiIiIiIiIiIiImjBXHzKcRx69+6N4zj57krP4LqEN28GnVvOhvK2pbxtKW97ytyW8ralvG0pb3vK3JbyNqX39cY0v20pb19zXF1doVMNDQ2Ul5dTX1/PgAED8t0d6QHu0g5DRq7LUvlS3plR3raylbfY0xzPjGqKLeVtS3nbyubfTGXeOeVtT/uF/qT5nRnVFFs9uZ50ZX1XR5iLL0WjUTZu3Eg0mskl5qTbAgH6HH64ru5sRXnbUt6mVL/zQHPclvK2pbxtKW97ytyW8jal/UJjmt+2lLevdWurRaNRGhsbs9UXkYxFo1Fqa2v1h9WIEwpRfuaZurqzEeVtS3nbUv22pzluS3nbUt62lLc9ZW5LedvSfqEtzW9bytvfurRgvmvXLh555BEuuOAChg4dSmlpKf3796dPnz5MmDCB66+/njfffDNXfRURERERERERERERyZmMPuZoampizpw53HfffdTX1zN69Gg+85nPUFVVRa9evdi6dSu1tbU89NBD/OhHP+LYY49lzpw5HHPMMbnuv4iIiIiIiIiIiIhIVmS0YH7ggQfSt29fvvOd73DJJZcwZMiQlLdzXZcXXniBX/7yl5xyyincf//9fO1rX8tqh0XAu5p2eXm5rqZtxXVprq3V1Z2tKG9bytuU6nceaI7bUt62lLct5W1PmdtS3qa0X2hM89uW8vY1x3U733IPP/wwl156KcFgMOMHrq2t5b333uPkk0/uTv8KQleuoiqSDbqyc2aydXVn5Z0Z5W2rJ1+93O80xzOjmmJLedtS3ray+TdTmXdOedvTfqE/aX5nRjXFVk+uJ11Z383oHOZTpkzp0mI5wMiRI4tisVwKUzQaZf369bo4iJVgkP4nnghdrAOyl5S3LeVtSvU7DzTHbSlvW8rblvK2p8xtKW9T2i80pvltS3n7Wpcu+plOY2MjO3fuzMZDiWREf1htOcEg/U44AUeF3oTytqW8bal+29Mct6W8bSlvW8rbnjK3pbxtab/Qlua3LeXtb91aMF+/fj0nn3wy/fv3Z8CAAZx00kmsW7cuW30TERERERERERERETHTrQXzq6++mgMOOICamhpefvllwuEwV111Vbb6JiIiIiIiIiIiIiJiJpTJjV544QVOOeWUPdpfeeUV1q9fT69evQC49dZb+cIXvpDdHoqkEAgEGDx4MIFAVs4qJJ1wo1Eaa2pw9VU5E8rblvK2pfptT3PclvK2pbxtKW97ytyW8ral/UJbmt+2lLe/ZVSVzj77bKZMmcLWrVvbtFdXV/Pyyy8nfn755ZfZd999s9tDkRQCgQCjRo3SH1Yr4TD1CxZAOJzvnvQMytuW8jal+p0HmuO2lLct5W1LedtT5raUtyntFxrT/LalvH0to6q0YsUK6urqGD16NI8//nii/dZbb+Xss89m4sSJfOpTn2L27NnMmjUrV30VSYhGo6xZs0YXB7ESClF+5pkQyuhLKdJdytuW8jal+p0HmuO2lLct5W1LedtT5raUtyntFxrT/LalvH0towXz/fffnz/+8Y/8+Mc/5rrrruOzn/0stbW1fOELX2DZsmVcdtllXH311bz55ptceOGFue6zCNFolE2bNukPqxEnEKDP4Yfj6JN/E8rblvK2pfptT3PclvK2pbxtKW97ytyW8ral/UJbmt+2lLe/deljjgsvvJDTTz+d66+/nrFjx/Ld736X6667jtGjR+eqfyIiIiIiIiIiIiIiJrr8MUdFRQU/+9nP+NOf/sSvf/1rxo0bx+uvv56LvomIiIiIiIiIiIiImMl4wfw3v/kNF198Meeddx4/+tGPOPLII6mpqeH888/n05/+NN/85jfZvn17LvsqkhAIBBg2bJguDmLEjUTY8fLLuJFIvrvSIyhvW8rbluq3Pc1xW8rblvK2pbztKXNbytuW9gttaX7bUt7+llFV+v73v88VV1xB//79OeCAA7jnnnu44IILKCkp4ZZbbmHp0qWsWLGCMWPG8NRTT+W6zyL6w2otEmH7Sy+BCr0N5W1LeZtS/c4DzXFbytuW8ralvO0pc1vK25T2C41pfttS3r6WUVX62c9+xv3338+8efP40Y9+xJ///GcWLFhAXV0dAAcddBAvvPACt912G1dccUVOOywCEIlEWLlyJREVHhNOSQkDL7oIp6Qk313pEZS3LeVtS/Xbnua4LeVtS3nbUt72lLkt5W1L+4W2NL9tKW9/y2jBvLm5mfLy8sTP/fv3x3VdWlpa2txuypQprFy5Mrs9FEnBdV3q6+txXTffXekZHIeykSPBcfLdk55BedtS3qZUv/NAc9yW8ralvG0pb3vK3JbyNqX9QmOa37aUt69ltGB+wQUXMG3aNO68807uv/9+Pv/5z3PkkUey33777XHbysrKrHcy7uOPP2by5MmUl5dTXl7O5MmT2bZtW6f3W7lyJWeffTbl5eX079+fo48+mvfeey9n/RQRERERERERERER/wllcqO7776bIUOG8NRTT7Fr1y6OPfZYZs2aleOu7eniiy9m/fr1LFy4EIArrriCyZMn8+yzz6a9z5o1azj++OO5/PLLufXWWykvL2flypX06tXLqtsiIiIiIiIiIiIi4gMZLZiXlZVxyy23cMstt+S6P2mtXLmShQsX8tprr3HUUUcB8NBDD3HMMcewevVqDjrooJT3u/nmmznjjDOYM2dOom3kyJEmfZbcCQQCjBw5UhcHMeKGw9QvWIAbDue7Kz2C8ralvG2pftvTHLelvG0pb1vK254yt6W8bWm/0Jbmty3l7W++qUqvvvoq5eXlicVygKOPPpry8nJeeeWVlPeJRqMsWLCA//iP/+C0006jqqqKo446iqefftqo15IrgUCAqqoq/WG1Eo3SWFMD0Wi+e9IzKG9bytuU6nceaI7bUt62lLct5W1PmdtS3qa0X2hM89uW8va1jI4wP+OMM7j99tsZN25cRg/a3NzMAw88QFlZGd/4xje61cG4uro6qqqq9mivqqqirq4u5X02btzIjh07+OEPf8gPfvAD7rzzThYuXMh5553HCy+8wEknnZS2/83NzYmfGxoaAAiHw4RjnwwFAgECgQDRaJRo0uSPt0cikTYXrkjXHgwGcRwn8bjJ7cAeV4tO1x4KhXBdt0274zgEg8E9+piu3U9jam1tZcWKFRx88MEEAoGiGFNyH53SUnBd3NZWCAZxYo8H4EajEA5DKISTtGPhRiIQiXhXYE66qIQbDkM0mr69tLRNH93WVnDdNu1OSQmDvvxlNv/yl7TntrRAIIATSion8b6na8/SmLK1nfbIII9jckpKcEpLGfSVr7Dl178m2tSU8XZK9N1x9rgSdzbGFI1Gs/J6IhAomDG50SiO41B5+eVsefRR7zbk9vWU6Zji87jYank0GuWtt95i7NixOI5TFGNq3/c227sAarlTUrK7puzcmdPXU1fGFK8p3d1OQMGMyQ2HcYJBKr/2Nbb8+te7a0oOakRXx5Stfdh4XwthTBDbR5k8mc2/+IXXP6N9o87GlK39csv9vc7GRCBAoHfvRD1xW1rM9o06G1M4HM5aLQcKYkzgbScnFNqzpuR436izMSXX5+7+zU3UlDyPKb49kmuK15Db/b1Mx+T3faN0dS8cDvPWW29x8MEHEwqFimJMye2EQubv3SH93MN1qZwyhS2PPZaoJ5bvc9ONKXlbdXc7tVlLyXMtj++Db/7FL3BbWszf56YdUyxHv72eslH32o+jIxktmFdXVzNx4kSOOuoovvKVr3DyySfvcQqU7du38/rrr/P73/+e+fPnU15ezqOPPtrpY8+aNYtbb721w9ssXrwYSNqBSeK6bsp2IBHgOeecw4wZMwA4/PDDeeWVV/jpT3+adsF89uzZKfu0bNky+vbtC8DgwYMZNWoUa9euZdOmTYnbDBs2jGHDhvHOO+9QX1+faB85ciRVVVWsWLGCpqamRPvo0aOpqKhg2bJlbSbO2LFjKS0tZcmSJW36MGHCBFpaWli+fHmiLRgMMnHiROrr61m1alWivXfv3hx22GFs3ryZ2traRHt5eTljxoxhw4YNrF+/PtHutzHV1dWxa9cuHMcpmjHFt1P1zJk019aydf58+h93HP1OOCFx+8aaGuoXLKD8tNPoc/jhifYdL7/M9pdeYp/zz/euxBxTv2ABjTU1VE6ZQijporxb58+nubaWIdOntyncm+bNI9LQQPXMmYk2JxDABUKDBlE5ZUqi3W1poW7uXMpGjGDgRRcl2sObN7Np3jz6jB1L+ZlnJtqzPaZsbafkseZ7TJVTplBSVUXZqFGUVFay5fHHM95OAHVz5xIcMIDBV16Z9TGtXbs2K6+nshEjCmZMjTU1NDz/PL3HjKH62mu9HYsMt9Pevp4yHVN8vhZbLXddl+3bt+O6Llu2bCmKMUHb7ZS8vQuhljuBQKKmfHjnnTl9PXVlTJs3b87KdnJKSwtmTPULFtD09tv0HTeOksrKRE3JRY3o6pjiuXX39RTvayGMCbx9lFBlJTgO5ZMmme0bdTambO2XW+7vdTamshEjGHTJJYl60rpxo9m+UWdjWrJkSdZqOVAQY4pvp+iuXfQ76qg2NSXX+0adjSk54+7+zY3PwXyPKb6dnECAQL9+4DhUfvWrOd/fy3RMft83Slf33n//fbZt28auXbuoqqoqijElb6fy004zf+8O6efeR/fdR+l++7V532P5PjfdmJK3SXe3U/XMmQUxJqe0NLEPvu3ppwlv2WL+PjfdmABfvp6yUfd27txJphy3zWF/6S1btowf/vCHPP3004TDYXr37s3gwYPp1asXW7duZcuWLbiuy/7778+0adP4xje+QVlZWaePu3nzZjZv3tzhbUaMGMFvfvMbrr32WrZt29bmdxUVFdxzzz189atf3eN+LS0t9O3bl+9973t85zvfSbTfcMMN/P3vf+cf//hHyudLdYT58OHD2bJlCwMGDACK5xNdv35K3dLSwhtvvMH48eMTz+f3MSX38b6+fQviqMREH0tLGTJ9OnV33QXtSkY+j8aeEQ5nZTvd3e4iwHk/wrysjCEzZvDRPfcQbWwsiCP4AGY0NWXl9XRvWVnBjMmNRnECAaqvv56P7r3Xe0zyfwSfEwoxPfbHvNhqeSQSYenSpUycODHRT7+PqX3f74ktuAAFUcud0tLdNWXHjoI4gg9215Tubqd7Y49bCGNyw2GcUGjPmlIAR2PHa0p3X0/3xQ4eKYQxQWwf5ZprqJszJ1HTEzfPYy3/VjSalf3yu0pKCmZMBAIE+vRJ1BO3ubkgjuADmL5zZ9Zq+T2hUEGMCbzt5JSUUH3DDV7m8ZqS56Oxpzc2Jtq7+zc3UVMK5Qjz+PueuXO99z0FcoT5tZGIr/eN0tW91tZWli5dyvjx4ykpKSmKMSW339u7d2EdYY63oPzRffcl6kkhHI09PWkRs7vbqc1aSr6PMI/tg9fNmYPb3FwwR5hfFw778vWUjbrX0NDAoEGDqK+vT6zvppPREeYA48aN4//9v//Hxo0b+fOf/8xrr73Ghg0baGpq4ogjjmD06NGcfPLJHHfccWmP+E6lsrKSyqRPZNI55phjqK+vZ9GiRRx55JEAvP7669TX13PsscemvE9paSkTJ05k9erVbdrfeecd9t9//7TPVVZWlnKxPxQKEQq1jSzxdcl2gkkTNZP29o+7N+2O46RsT9fHrrYX2pjiL6Lk3/t9TPE+Jv54AUQiia/NtBEOk+rTrsRXqzJtT36uztpdN3V7NNq19iyNKVvbqZDG5La2en90488T+0NQCNspPj+7/XoqoDEBEP/aXkvLHvfJ5+sp0783fqx78f2EYhpTch9zXiNStXc295JfEwVSy+PbMivbqUDG1L4/e9SUPNa9bO3DFtKYkh8XKKh9o2ztlxfSmJL3TRKnY0lub8+w7iVnnY1aXghjStWfNr/PY91Lldne1vJCGVMqhfT+ye/7Run6nnzwW9bea+xF39O1d7uWxxcZC2TuJZ8CaY/75PH1lCrjvd1ObcZQCDUiGk3spxTSGosvX0+dtGcypnT9TSXzW8ZUVVUxefJkJk+e3NW7dsuYMWM4/fTTmTp1KvPmzQPgiiuu4KyzzmpzepjRo0cze/Zszj33XABmzpzJl770JU488UROOeUUFi5cyLPPPsuLL75o2n/JrmAwyOjRo9O+ICS73HCYrfPn6+rORpS3LeVtS/Xbnua4LeVtS3nbUt72lLkt5W1L+4W2NL9tKW9/89WliB9//HEOPfRQJk2axKRJkxg7duwe50lfvXp1m3PWnHvuufz0pz9lzpw5HHroofz85z/niSee4Pjjj7fuvmSR4zhUVFR06dsM0g3RKM21tbq6sxXlbUt5m1L9zgPNcVvK25bytqW87SlzW8rblPYLjWl+21LevuarBfOBAwfy2GOP0dDQQENDA4899hgVFRVtbuO6LpdddlmbtilTpvDuu+/S1NRETU0N55xzjl2nJSfC4TCLFy/u0hVuZe85paXehePanXNLckN521LetlS/7WmO21LetpS3LeVtT5nbUt62tF9oS/PblvL2N18tmIska38RAcktFXlbytuW8ral+m1Pc9yW8ralvG0pb3vK3JbytqX9Qlua37aUt39pwVxEREREREREREREBC2Yi4iIiIiIiIiIiIgAEMr2A7quqws2SM4Fg0HGjh2rq2kbcVtb2TRvHm5ra7670iMob1vK25bqtz3NcVvKu2MR4K/AEqABGAgcCxyfwX1/C7zRvrGlBW6/HYBpwP5AFHgZeAf4CGgE9gEOBk4BeifdvRV4FliO98boKOBUIPndzMfAXcBlwIEZ9LOYaX7bU+a2lLct7Rfa0vy2pbz9LasL5r/5zW+47bbbWLVqVTYfViSlUp0Lyo7rEmloANfNd096BuVtS3mbU/02pjluS3l36ElgKXAaMBxYDTwDNAOf6eS+pwLHpGj/Jd6bmuGxn1uB54HD8RbA+wLr8Rbq3wamAyWx274IrADOA3YBT+Mt4h/Rrs+HosVyQPM7H5S5LeVtTvuFhjS/bSlvX8v4lCz19fX86le/Ys6cOTz99NNEo9HE75588kkOOeQQvvzlL9Pc3JyTjooki0QiLFmyRBcIMaKrO9tS3raUty3Vb3ua47aUd3p1wGLgs8DJwCjgDOBIvMXsxk7uX4l3BHnyv0goxE5gYiCQeGNTAtwEnA+MjT3PSbGfP8I7mjxuJd7R7WNj/Rgfa4urAd4DPtfFsRYrzW97ytyW8ral/UJbmt+2lLe/ZXSE+b/+9S9OOOEENm7cmDjlykknncTTTz/NRRddxMKFC6moqGDOnDl885vfzHWfRURERETEZ94CXGBCu/YJwOt4R5uP6+JjLopGcYCJwSDEDugJ4B1V3l78CPT6pLYwkPw2thTYEfv/JuD3eIvlqR5PRERERIpTRgvm3/3ud2loaGDWrFlMmDCB2tpabr/9do499ljefvttvva1rzFnzhwqKipy3F0REREREfGjOryF5wHt2ocm/b4rmoDl0SgHH3AAgz74gM6+8Pyv2H+HJLXtDyzCO795M/Amu8+nvgCoZs8FfhEREREpbhktmP/tb3/jO9/5DjfddFOi7ZOf/CT/+Z//yVVXXcVPfvKTnHVQRERERET8byfQJ0V7KRCM/b4ravDOV37SYYfBBx90eNt64I/AMGBMUvskvHOg3xH7eTTegnkt3rnWZ3SxTyIiIiLifxktmG/atInjjjuuTdvxx3vHXnzpS1/Kfq9EOhEMBpkwYYKupm3EbWmhbu5c3JaWfHelR1DetpS3LdVve5rjtpS3p/3ZaOPnF3c6uE9Hv0tlEd4C/PAFC3BbW9PerhH4Rez/v0zbizj1B74JfIy3aF+Od5qWJ/AuQjoY75znzwENwAi8C4RWdLGvxULz254yt6W8bWm/0Jbmty3l7W8ZXfQzEonQq1evNm3xn/v375/9XolkoEVFx47jEBwwAJyuvpWVvaK8bSlvc6rfxjTHbSlvtgI3tvtXi3c6llRHkbfgLbCnOvo8nQ3AeryLdJaVl6fNuxH4Gd4R5lOBQSlu4wAD8RbLAV6ItZ0MbATmA2cB34n1cX4X+ll0NL/tKXNbytuc9gsNaX7bUt6+ltGCOcDq1atZunRpm38Aq1atStkukkuRSITly5fratpGnJISBl95JU5JSb670iMob1vK25bqtz3NcVvK2ztH+X+1+/cJvPOB78Q7UjvZh7H/VnfhORbH/ntUKJQ27/hi+VbgCnafK70jG/EWzM/HO+L8Xbxzno/GO3XMiXiL/81d6Gsx0fy2p8xtKW9b2i+0pfltS3n7W0anZAG47LLLUrZPnjw58f+u6+I4joqdiIiIiEgPFQKGp2g/GPgz8AZwSlL7EqAEOCjDxw/jnV98OLBvIPXxP8mL5VPxFuwz8QTeRT5HxH528Y6Aj2tJahcRERGR4pTRgvkvf/nLXPdDRERERESKWDUwEe984A7egvc7wOvAabQ9JcvzwF/wjgwf1e5xVuAtiJ+R5nlagZ/jnbblbCAKrEv6fV+gMsX9FgGbgcuS2g4EnsVb6D8g1vcRQC9EREREpFhltGB+6aWX5rofIl2mC4PY0oUqbClvW8rbluq3Pc1xW8o7vfPwzhX+D2A73rnDzwaOb3c7F2+hO5VFeKdHOSx+23Z5bwfej/3/71Pc/wjgwnZtO4AFwBeA3kntQ2K3fQ54GdgP73QtPZnmtz1lbkt529J+oS3Nb1vK278c13X1jcJONDQ0UF5eTn19PQMGDMh3d6QHuEsXhcjIdVkqX8o7M8rbVrbyFnua45lRTbGlvG0pb1vZ/JupzDunvO1pv9CfNL8zo5piqyfXk66s72Z8DvNkkUiEP/3pT6xcuZKmpqY2v3Mch+9+97t787AiGXNdl/r6esrLy3FUEHMvEKBsxAia//1viKY73kuyRnnbUt6mVL/zQHPclvK2pbxtKW97ytyW8jal/UJjmt+2lLevpb5KTge2bNnCoYceytlnn82NN97IrFmzmDVrFrfeeiu33nors2bNykE3RdqKRCKsWrVKF5g14oRCDLzoIpzQXn3GJl2kvG0pb1uq3/Y0x20pb1vK25bytqfMbSlvW9ovtKX5bUt5+1uXt9rNN99Mr169WLduHfvvvz+vv/46AwcO5Kc//Sl/+MMf+Mtf/pKLfoqIiIiISJ4V2td4w+EwS5Ys4UuzZhHSG1IRERERyYIuH2H+17/+lWuvvZahQ4d6DxAIMGrUKObOncupp57Kddddl/VOioiIiIiIiIiIiIjkWpcXzNevX8+IESMIBoMEAgF27tyZ+N3nPvc5nn/++ax2UCQVx3Ho3bu3znNmxXUJb94MBXZUWdFS3raUtynV7zzQHLelvE2pphjT/LanzG0pb1Oq4cY0v20pb1/r8oJ5ZWUl9fX1AAwdOpQVK1Ykfrd161bC4XD2eieSRjAY5LDDDiMYDOa7Kz2C29rKpnnzcFtb892VHkF521LetlS/7WmO21LetlRTbGl+21PmtpS3LdVwW5rftpS3v3V5wfyII47grbfeAuCMM87gtttu47HHHuN//ud/+Pa3v83RRx+d9U6KtBeNRtm4cSNRXWnYRiBAn8MPh0CXS4bsDeVtS3mbUv3OA81xW8rblGqKMc1ve8rclvI2pRpuTPPblvL2tS5vtWnTplFeXg7A97//faqrq/nKV77ChRdeSDAY5L777st6J0Xai0aj1NbW6g+rEScUovzMM3V1ZyPK25bytqX6bU9z3JbytqWaYkvz254yt6W8bamG29L8tqW8/S2jrfb5z3+er33ta5xxxhmceuqpnHrqqQAMHjyYZcuWsWLFChzHYfTo0bo6vYiIiIiIiIiIiIj4UkZHmD/33HOcc845DBs2jJtuuol333038TvHcTj00EM55JBDtFguIiIiIiIiIiIiIr6V0YL5Rx99xIMPPsh+++3HnXfeyejRoznppJP41a9+RWNjY677KLIHx3EoLy/X1bStuC7NtbW6urMV5W1LeZtS/c4DzXFbytuUaooxzW97ytyW8jalGm5M89uW8vY1x3W7tuVWrVrFww8/zGOPPUZdXR39+/fnwgsvZMqUKRx11FG56mdeNTQ0UF5eTn19PQMGDMh3d6QHuEs7DBm5Lkt/eJR3ZpS3rWzlLfY0xzOjmmJLNcWfNL8zk835rcw7p7ztqYb7k+Z3ZlRTbPXketKV9d0uX/Rz9OjRzJkzh/fff5/f//73fOYzn+GRRx7h2GOP5eCDD+buu+/e646LZCoajbJ+/XpdHMRKMEj/E0+EYDDfPekZlLct5W1K9TsPNMdtKW9TqinGNL/tKXNbytuUargxzW9bytvXurxgHhcMBvnc5z7Hk08+yQcffMC1117LqlWrmDlzZjb7J5KS/rDacoJB+p1wAo4KvQnlbUt521L9tqc5bkt521JNsaX5bU+Z21LetlTDbWl+21Le/rbXC+YA4XCYJ598kssuu4z77rsP13U5/PDDs9S1PX388cdMnjyZ8vJyysvLmTx5Mtu2bevwPjt27GDatGkMGzaM3r17M2bMGB588MGc9VFERERERERERERE/Cm0N3f65z//ycMPP8zjjz/O5s2b2Weffbjyyiu5/PLLc7pgfvHFF7N+/XoWLlwIwBVXXMHkyZN59tln095nxowZvPDCCzz22GOMGDGC5557jquvvpqhQ4dyzjnn5KyvIiIiIiIiIiIiIuIvGS+Y19fX8/jjj/PLX/6SpUuXAnDKKadw+eWXc95551FWVpazTgKsXLmShQsX8tprryUuLvrQQw9xzDHHsHr1ag466KCU93v11Ve59NJLOfnkkwFvkX3evHksWbJEC+Y+FggEGDx4MIFAt74kIRlyo1Eaa2pw9VU5E8rblvK2pfptT3PclvK2pZpiS/PbnjK3pbxtqYbb0vy2pbz9LaOqdPHFF7Pvvvsybdo0PvroI26++WbWrFnDX/7yFy666KKcL5aDt/BdXl6eWCwHOProoykvL+eVV15Je7/jjz+eZ555hg8++ADXdXnhhRd45513OO2003LeZ8mdQCDAqFGj9IfVSjhM/YIFEA7nuyc9g/K2pbxNqX7ngea4LeVtSjXFmOa3PWVuS3mbUg03pvltS3n7WkZHmD/xxBOcffbZXH755Zx22mk4jpPrfu2hrq6OqqqqPdqrqqqoq6tLe78f//jHTJ06lWHDhhEKhQgEAvz85z/n+OOPT3uf5uZmmpubEz83NDQA3jnbw7GJHggECAQCRKPRNheoiLdHIhFc1+20PRgM4jhO4nGT2wEikUhG7aFQCNd127Q7jkMwGNyjj+na/TSmcDjM2rVr2X///QkEAkUxpuQ+OqWl4Lq4ra0QDLa5SIQbjXoFNxTCSdqxcCMRiERwSkog6TXqhsMQjaZvLy1t00e3tRVct217KMSAU06h/rnn9nj9uy0tEAjghJLKSbzv6dqzNKZsbac9MsjjmJySEigpYcCpp9Lwl7/g7tqV+XaK991xvMfJ8pii0WhWXk8EAgUzpvin/eX/+Z80/PWviZ2ZnL6eMhxTfB4XWy2PRqOsW7eOUaNGJX72+5ja973N9i6EWh4K7a4pjY25fT11YUzxmtLd7QQUzJjccBgCAcrPOIOGv/xld03JQY3o6piKdR+2o5ri1zEl99F0f48M5l6vXol6Qmur3b5RJ2MKh8NZ205AQYwJSDznHjUl1/tGnYwp+TXS3ddTfMz5HlNie4RCDPj0p6lfuNCbDzne38t0TH7fN0pX98LhMOvWrWP//fdPrNf4fUxtLmAaCtm/d6eDuReNUn766TT83//tXsS1fJ+bZkzJ26q726nNWkq+a3lsH7z+T3+C1lb797npxhTL0W+vp2zUvfbj6EhGC+YffPABlZWVGT9oV8yaNYtbb721w9ssXrwYSNqBSeK6bocL+D/+8Y957bXXeOaZZ9h///156aWXuPrqq9l333059dRTU95n9uzZKfu0bNky+vbtC8DgwYMZNWoUa9euZdOmTYnbDBs2jGHDhvHOO+9QX1+faB85ciRVVVWsWLGCpqamRPvo0aOpqKhg2bJlbSbO2LFjKS0tZcmSJW36MGHCBFpaWli+fHmiLRgMMnHiROrr61m1alWivXfv3hx22GFs3ryZ2traRHt5eTljxoxhw4YNrF+/PtHupzFt2rSJf/3rX2zevBnHcYpiTMnbqXrmTJpra9k6fz79jzuOfieckLh9Y00N9QsWUH7aafRJumbAjpdfZvtLL7HP+edTNnJkor1+wQIaa2qonDKFUNLreOv8+TTX1jJk+vQ2hXvTvHlEGhqonjkz0eYEArhA49KlVE6Zkmh3W1qomzuXshEjGHjRRYn28ObNbJo3jz5jx1J+5pmJ9myPKVvbKXms+R5T5ZQplFRVUTZqFH0PO4wtjz+e8XYCqJs7l+CAAQy+8sqsj2nt2rVZeT2VjRhRMGNqrKmh4fnnKT/1VPoefnhiAT2Xr6dMxxSfr8VWy13XZfv27RxwwAFs3bq1KMYEbbdT8vYuhFruBAKJmvLhnXfm9PXUlTFt3rw5K9vJKS0tmDHVL1hA09tvs89ZZ9H3sMMSNSUXNaKrY4rn5rfXU2c1wnVdmpqaOOCAA1i3bl1RjAl2byfL/T3ofO4NuuSSRD1p3bjRbN+oszEtWbIka9sJKIgxxbdTdNcuBp53Xpuakut9o87GlJxxd19P8TmY7zHFt5MTCBDo14+G556j8qtfzfn+XqZj8vu+Ubq69/7777Nt27bEPkExjCl5O5Wfdpr5e3dIP/c+uu8++h1zDH3HjUvUE8v3uenGlLxNurudqmfOLIgxOaWliX3wxsWLCW/ZYv4+N92YAF++nrJR93bu3EmmHLfNYX+pffzxx3zta1/jq1/9KmeddVbK2/zhD3/gl7/8JT/72c8YNGhQxh3YvHkzmzdv7vA2I0aM4De/+Q3XXnst27Zta/O7iooK7rnnHr761a/ucb+mpibKy8t56qmnODNpQn3ta19rc/HQ9lIdYT58+HC2bNnCgAEDgOL5RNevn1K3tLTwxhtvMH78+MTz+X1MyX28r2/fwjgqMd7H0lKGTJ9O3V13QbuSkc+jsWeEw1nZTnf36lUwY3JKSnDKyhgyYwYf3XMP0cbGvB+VGDejqSkrr6d7y8oKZkxuNIoTCFB9/fV8dO+93mNSAEfwhUJMj/0xL7ZaHolEWLp0KRMnTkz00+9jat/3e2ILLkBB1HKntHR3TdmxozCO4GN3Tenudro39riFMCY3HMYJhfasKQVwhHm8pvjt9dRZjeiopvh1TMl9vKukpDCOSozNvUCfPol64jY3F8YRfMD0nTuztp3uCYUKYkzgbSenpITqG27wMo/XlDwfjT29sTHR3t3X032xA9LyPab49ki875k713vfUyBHmF8bifh63yhd3WttbWXp0qWMHz+ekpKSohhTcvu9vXsX1BHm4C0of3TffYl6UghHY09PWsTs7nZqs5aS51oe3wevmzMHt7m5YI4wvy4c9uXrKRt1r6GhgUGDBlFfX59Y300noyPMf/7zn/Pmm29y+umnp73N6aefzrXXXssDDzzALbfcksnDAlBZWZnR0evHHHMM9fX1LFq0iCOPPBKA119/nfr6eo499tiU92ltbaW1tZVA0uQAEsGnU1ZWlvK87KFQiFCobWSJr0u2E0yaqJm0t3/cvWl3HCdle7o+drW90MYUfxEl/97vY4r3MfHHCyASSXxtpo1wmFSfdrmtrSn7krY9+bk6a3fd1O3RaNfaszSmbG2nQhqT29rq/dGNP0+sVhXCdorPz26/ngpoTADEv7bX0rLHffL5esr0740f6178m2HFNKbkPua8RqRq72zuJb8mCqSWx7dlVrZTgYypfX/2qCl5rHvFvA/bWU3x45gSCnF/L/bfxHNZ7Bt10vfkrLOxnQphTKn60+b3eax7qTLb29dToYwplUJ6/+T3faN0fU8++C1r7zX2ou/p2rtdy+OLjAUy95JPgbTHffL4ekqV8d5upzZjKIQaEY0SP+iwkNZYfPl66qQ9kzGl628qGV1Z4be//S1Tp07t8IFDoRBTp07lmWeeyfjJu2LMmDGcfvrpTJ06lddee43XXnuNqVOnctZZZ3HQQQclbjd69GieeuopAAYMGMBJJ53EzJkzefHFF1m7di2PPPIIv/71rzn33HNz0k+xEQgEGDZsWOo3FpJ1biTCjpdfTl2AJeuUty3lbUv1257muC3lbUs1xZbmtz1lbkt521INt6X5bUt5+1tGVemdd95hwoQJnd5u/PjxvPPOO93uVDqPP/44hx56KJMmTWLSpEmMHTuWRx99tM1tVq9e3eacNb/97W+ZOHEil1xyCZ/61Kf44Q9/yO23385VV12Vs35K7ukPq7FIhO0vvQQq9DaUty3lbUr1Ow80x20pb1OqKcY0v+0pc1vK25RquDHNb1vK29cyqkrhcJiSFOc+aq+kpITWdF9NzYKBAwfy2GOP0dDQQENDA4899hgVFRVtbuO6Lpdddlni5+rqan75y1/ywQcf0NTUxKpVq7j22ms7vFCoFL5IJMLKlSv3OC+S5IZTUsLAiy5KeQ40yT7lbUt521L9tqc5bkt521JNsaX5bU+Z21LetlTDbWl+21Le/pbRgvm+++7L22+/3ent3nrrLaqrq7vdKZHOuK5LfX19mxP6Sw45jndVaH3QZEN521LeplS/80Bz3JbyNqWaYkzz254yt6W8TamGG9P8tqW8fS2jBfOTTjqJn/zkJx0ePd7a2sqDDz7IKaeckrXOiYiIiIiIiIiIiIhYyWjBfMaMGaxatYpzzz2XDRs27PH7DRs28PnPf57Vq1czY8aMrHdSRERERERERERERCTXQpncaOzYsTzwwANcffXVHHDAARxxxBEccMABAKxdu5Y33niDaDTKgw8+yKGHHprTDouAd3GQkSNH6uIgRtxwmPoFC3DD4Xx3pUdQ3raUty3Vb3ua47aUty3VFFua3/aUuS3lbUs13Jbmty3l7W8ZLZgDTJ06lUMOOYQ77riDF154gddeew2APn36cPrpp3PTTTdx9NFH56yjIskCgQBVVVX57kbPEY3SWFOT7170HMrblvI2pfqdB5rjtpS3KdUUY5rf9pS5LeVtSjXcmOa3LeXta136GO+YY47h2WefpaGhgbq6Ourq6mhoaOD3v/+9FsvFVCQS4c0339TVtI04JSUMvvJKXd3ZiPK2pbxtqX7b0xy3pbxtqabY0vy2p8xtKW9bquG2NL9tKW9/y/gI82T6FFDyzXVdmpqadDVtK45DqLJSV3e2orxtKW9Tqt95oDluS3mbUk0xpvltT5nbUt6mVMONaX7bUt6+phNFiYiIiIiIiIiIiIigBXMREREREREREREREUAL5uJTwWCQ0aNHEwwG892VHsENh9k6f76u7mxEedtS3rZUv+1pjttS3rZUU2xpfttT5raUty3VcFua37aUt7/t1TnMRfLNcRwqKiry3Y2eIxqlubY2373oOZS3LeVtSvU7DzTHbSlvU6opxjS/7SlzW8rblGq4Mc1vW8rb13SEufhSOBxm8eLFhPVJnQmntJTqmTNxSkvz3ZUeQXnbUt62VL/taY7bUt62VFNsaX7bU+a2lLct1XBbmt+2lLe/acFcfCsSieS7Cz2Kirwt5W1LedtS/banOW5LedtSTbGl+W1PmdtS3rZUw21pfttS3v6lBXMREREREREREREREbRgLiIiIiIiIiIiIiICaMFcfCoYDDJ27FhdTduI29rKpnnzcFtb892VHkF521LetlS/7WmO21LetlRTbGl+21PmtpS3LdVwW5rftpS3v2nBXHyrVOeCsuO6RBoawHXz3ZOeQXnbUt7mVL+NaY7bUt7mVFMMaX7bU+a2lLc51XBDmt+2lLevacFcfCkSibBkyRJdIMSIru5sS3nbUt62VL/taY7bUt62VFNsaX7bU+a2lLct1XBbmt+2lLe/acFcRERERERERERERAQtmIuIiIiIiIiIiIiIAFowFxEREREREREREREBwHFdnX2+Mw0NDZSXl1NfX8+AAQPy3R0BXNclEokQDAZxHCff3cm6uwpwTE5pKW5LS7670cZ1WSpfyjszyttWtvIuNMVev0FzPFOqKbZUU/xJ8zsz2Zzfyrxzytuearg/aX5npphrSrHn7TddWd/VEebiWy0FVnSKmuMQHDAACuyPT9FS3raUtznVb2Oa47aUtznVFEOa3/aUuS3lbU413JDmty3l7WtaMBdfikQiLF++XFfTNuKUlDD4yitxSkry3ZUeQXnbUt62VL/taY7bUt62VFNsaX7bU+a2lLct1XBbmt+2lLe/acFcRERERERERERERAQtmIuIiIiIiIiIiIiIAFowFx8LBoP57kKPUmgXqih2ytuW8ral+m1Pc9yW8ralmmJL89ueMrelvG2phtvS/LalvP3Lcd0efHnUDHXlKqoi2VBoV3YuVNm6urPyzozyttWTr17ud5rjmVFNsaWa4k+a35nJ5vxW5p1T3vZUw/1J8zszqim2enI96cr6ro4wF19yXZdt27ahz3uMBAKUjRwJAZUME8rblvI2pfqdB5rjtpS3KdUUY5rf9pS5LeVtSjXcmOa3LeXta9pq4kuRSIRVq1bpatpGnFCIgRddhBMK5bsrPYLytqW8bal+29Mct6W8bamm2NL8tqfMbSlvW6rhtjS/bSlvf9OCuYiIiIiIiIiIiIgIPlswv/322zn22GPp06cPFRUVGd3HdV1mzZrF0KFD6d27NyeffDJvvfVWbjsqIiIiIiIiIiIiIr7jqwXzlpYWLrjgAr7+9a9nfJ85c+Zw9913c//997N48WKqq6v57Gc/y/bt23PYU8k1x3Ho3bs3ji7oYMN1CW/eDDq3nA3lbUt5m1L9zgPNcVvK25RqijHNb3vK3JbyNqUabkzz25by9jXH9eHVFR555BGuueYatm3b1uHtXNdl6NChXHPNNdxwww0ANDc3M2TIEO68806uvPLKjJ6vK1dRFckGXdk5M9m6urPyzozyttWTr17ud5rjmVFNsaWa4k+a35nJ5vxW5p1T3vZUw/1J8zszqim2enI96cr6blGfeX7t2rXU1dUxadKkRFtZWRknnXQSr7zyStoF8+bmZpqbmxM/NzQ0ABAOhwmHwwAEAgECgQDRaJRoNJq4bbw9Eom0udJzuvZgMIjjOInHTW4H9rj4Rbr2UCiE67pt2h3HIRgM7tHHdO1+GlM4HGbTpk0MGjSIQCBQFGNK7qNTWgqui9vaCsEgTuzxANxoFMJhCIVwkq627EYiEInglJRA0h8JNxyGaDR9e2lpmz66ra3gum3bAwF6jx5N4z//2aYvAG5LCwQCbS9kEe97uvYsjSlb22mPDPI4JqekBIJB+hx6KI3//KfXl0y3U7zvjuM9TpbHFI1Gs/J6IhAomDG50ShEo/QZN46mt96C2Lhy+nrKcEzxeVxstTwajbJ161aqqqoSP/t9TO373mZ7F0ItDwR215Rdu3L7eurCmOI1pbvbCSiYMbmxPvcZP56mFSt215Qc1IiujqlY92E7qil+HVNyH03398hg7pWWJuoJkYjdvlEnYwqHw1nbTkBBjAli28lx6HvEEV7m8ZqS632jTsaU/Brp7uspPuZ8j4mkbd/7U5+isabGe4wc7+9lOia/7xulq3vhcJgtW7YwaNAgQqFQUYwpuZ1QyP69Ox3MvXCYPocdRtPKlYl6Yvo+N82YkrdVd7dTm7WUfNfy2D74zqVLvcexfp+bbkyxHP32espG3Ws/jo4U9YJ5XV0dAEOGDGnTPmTIENatW5f2frNnz+bWW2/do33ZsmX07dsXgMGDBzNq1CjWrl3Lpk2bErcZNmwYw4YN45133qG+vj7RPnLkSKqqqlixYgVNTU2J9tGjR1NRUcGyZcvaTJyxY8dSWlrKkiVL2vRhwoQJtLS0sHz58kRbMBhk4sSJ1NfXs2rVqkR77969Oeyww9i8eTO1tbWJ9vLycsaMGcOGDRtYv359ot1PY9q0aRM1NTVUVFTgOE5RjCl5O1XPnElzbS1b58+n/3HH0e+EExK3b6ypoX7BAspPO40+hx+eaN/x8stsf+kl9jn/fMpGjky01y9YQGNNDZVTphCqrEy0b50/n+baWoZMn96mcG+aN49IQwPVM2cm2pxAABdo/egjKqdMSbS7LS3UzZ1L2YgRDLzookR7ePNmNs2bR5+xYyk/88xEe7bHlK3tlDzWfI+pcsoUSqqqKBs1iuY1a9jy+OMZbyeAurlzCQ4YwOCkDwSzNaa1a9dm5fVUNmJEt8b0uxdfZN2HH1JbW0tDNMoJo0dz5ec+l/GYQkcdxe9efJHXV65kR2MjVa7L+YccwlFnnOHtWMS201UvvQStraRS3bcvd33jGwC0hMM8/OCDLGtooLS0lE+PH8/njz8ex3ESYwpdcQU3zJvHjAsu4JADDshoO8Xna7HVctd12b59O5/+9KfZunVrUYwJ2m6n5DlcCLXcCQQSNeXDO+/MWY3o6pg2b96cle3klJYWzJjqFyyg6e23qbriClrefz9RU7ryNxdyU8vjufnt9dRZjXBdl6amJiorK1m3bl1RjAl2byfL/T3ofO4NuuSSRD1p3bjRbN+oszEtWbIka9sJKIgxxbdTdNcuqr7xDZrXrEnUlFzu72UypuSMu/t6is/BfI8pvp2cQIBAv340rVhB5Ve/mtPXU1fG5Pd9o3R17/3332fbtm1UVFRQVVVVFGNK3k7lp51m/t4d0s+9j+67j33OP5+KXbsS9cTyfW66MSVvk+5up+qZMwtiTE5paWIffP1NNxHessX0vXtHYwJ8+XrKRt3buXMnmcr7KVlmzZqVcnE62eLFi5kwYULi50xPyfLKK69w3HHHsWHDBvbdd99E+9SpU3n//fdZuHBhyvulOsJ8+PDhbNmyJXHIfrF8ouvXT6lbWlp44403GD9+fOL5/D6m5D7e17dvYRyVGO9jaSlDpk+n7q67aH/+rXwejT0jHM7Kdrq7V6+CGZNTUoJTVsaQGTP46J57iDY25v2oxLgZTU1ZeT3dW1bWrTHd1NLCUMdhX9dlMTAuEODCeP8zGNND0Sjvuy5nBINUui41wOvRKJeUlTEu1k83EmFdJLLHkUXrIhGecV0+HQhwRuzxn4tEeDUS4fNAczDIU5EI5weDHBEMJsb0C8ehr+NwUew+mWyn6bE/5sVWyyORCEuXLmXixImJfvp9TO37fk9swQUoiFrulJburik7dhTGEXzsrind3U73xh63EMbkhsM4oRDV11/PR/fe6/WB/B6VGB9TvKb47fXUWY3oqKb4dUzJfbyrpKQwjkqMzb1Anz6JeuI2NxfGEXzA9J07s7ad7gmFCmJM4G0np6SE6htu8DKP15Q8H409vbEx0d7d19N9sQPS8j2m+PZIvO+ZO9d731MgR5hfG4n4et8oXd1rbW1l6dKljB8/npKSkqIYU3L7vb17F9QR5uAtKH90332JelIIR2NPT1rE7O52arOWkudaHt8Hr5szB7e5uWCOML8uHPbl6ykbda+hoYFBgwb545Qs06ZN48ILL+zwNiNGjNirx66urga8I82TF8w3bty4x1HnycrKyigrK9ujPRQKEQq1jSzxdcl2gkkTNZP29o+7N+2O46RsT9fHrrYX2pjiL6Lk3/t9TPE+Jv54gfd113bFBoBwmFSfdrlpjohN2578XJ21u27q9mi0a+1ZGlO2tlMhjSn+1dvE80STvnqbad9ztJ3i87Pbr6dujukHQCD2B28p3s7AHvdJM6aVkQjvABcD42JjO7C0lJ2f+ATPrl3LWEhcDXt/gHbb9FXAASYmPf5K4HhgbOz264C3IxHGx+5bA7znulzffrt0sp0y/Xvjx7oX/8p7MY0puY85rxGp2jt7PSW/Jgqklse3ZVa2U4GMqX1/2j9HPmt5Me/DdlZT/DimhELc34v9N/FcFvtGnfQ9OetsbKdCGFOq/nRlPyLj9r0YU6rM9vb1VChjSqWQ3j/5fd8oXd+TD37L2nuNveh7uvZu1/L4ImOBzL3kUyBl+v7J4vWUKuO93U5txlAINSIaJX7QYSGtsfjy9dRJeyZjStffVPK+YF5ZWUll0lcYsumAAw6gurqa559/nnHjxgHQ0tLC3/72N+68886cPKfYiJ+GRVfTNuK6NNfWJgq95Jjy7lCK5YSMrQDKiC1ux7kux1ZXs2LtWt4DRqS57y5gOTASSP6rFQaSjxUoBXbE/r8J+D3wOaBvN/pdTFS/80A1xZbyNqWaYkzz254yt6W8TamGG9P8tqW8fa076w7m3nvvPWpqanjvvfeIRCLU1NRQU1PDjh07ErcZPXo0Tz31FOAV32uuuYY77riDp556ihUrVnDZZZfRp08fLr744nwNQ7IgGAwyZsyYtJ8gSXa5ra1snT8//ZF0klXKO3fqgCoguXK4ra30f/XVxO/TeRNoAY5s174/sAj4OHb/N2NtAAuAamACEqf6bU81xZbytqWaYkvz254yt6W8bamG29L8tqW8/c1XC+a33HIL48aN43vf+x47duxg3LhxjBs3rs1J5levXt3mJO/XX38911xzDVdffTUTJkzggw8+4LnnnqN///75GIJkSTQaZf369W2vQC25EwzS/8QTQTsyNpR3zjQCvds3BoNUHXVU4vfpLIrd99B27ZPwvq51B/AjYCjeKVpq8U4Zc153O11kVL/zQDXFlvI2pZpiTPPbnjK3pbxNqYYb0/y2pbx9zVcL5o888giu6+7x7+STT07cxnVdLrvsssTPjuMwa9YsPvzwQ3bt2sXf/vY3DjnkEPvOS1bpD6stJxik3wkntLmIhOSO8vZE2v3L1hfZ2n/h0wkG6RNbME+nDngPGAe0v3ROf+CbwE3Ad4DLY8/xBPAZYDDeqVzuAm4BHga2dWcAPqf6bU81xZbytqWaYkvz254yt6W8bamG29L8tqW8/S3v5zAXERFpbyswu13bVcCobj5uH1IfRb5z167E71NZFPtv+9OxxDnAwKSfX4i1nQxsBOYDl+Kd//zJ2M9fz7zbIiIiIiIiImJEC+YiIlJwBgD/1a5tcBYed1+gBu+I9eTP+d/fuBHwzjfeXhjv1CrDgE9k8Bwb8RbMr4g9x7vAEGB07PcnAvcAzXgXIBURERERERGRwuGrU7KIxAUCAQYPHkwgoClswY1GaaypwdVX5Uwob+/T3OHt/vXKwuMegrdQ/c+kNjca5cV//IMBwH4p7vM2sBOYmOFzPIF3kc8R8cfHu1hoXEtSe0+k+m1PNcWW8ralmmJL89ueMrelvG2phtvS/LalvP1NR5iLLwUCAUaN6u7JGSRj4TD1Cxbkuxc9h/Lu0Bq8BWyAKN75wJfHfh4J9Iv9//PAX/CO9I5Xi9HAgXinRWkGBgE14TBvbdrERaT+FHkR3nnLx2XQt0XAZuCypLYDgWeBPwMHAM/hLaZn4wMAP1L9zgPVFFvK25RqijHNb3vK3JbyNqUabkzz25by9jV9jCe+FI1GWbNmjS4OYiUUovzMMyGkz9hMKO8OPQc8GvsXxltAj//8UdLtXLwF9fYuBY7AW8D+Od7FPL82bhzjU+S9DXgHGAv07qRfO4AFwDntbjsEuBDvVDC/BkqBizp5rGKm+p0Hqim2lLcp1RRjmt/2lLkt5W1KNdyY5rct5e1rWjAXX4pGo2zatEl/WI04gQB9Dj8cR1+VM6G8O/Z1YG6af8nHp0xK0QbeecPPAW4Bfgh8q7SUk884I2XeFcAcvAXvzvQDbsVbXG9vHHAD8AO8I94HprhNT6H6bU81xZbytqWaYkvz254yt6W8bamG29L8tqW8/U0fc4iIiIhIj7QQ2AB8ADTgffskkw/IAHbhnfZpQ+zfTuCzwGmd3M8FHgTWAscC5yb9rhXvFE7L8XbSjwJOBZyk23wM3IV36qcDM+yriIiIiIhkTgvmIiLdoMUWERH/egnYF/gUsLiL920EXgeGAgfjXcMgE68AW9L87kVgBXAe3t+Ip/G+kXJE0m2eBA5F9VtEREREJFe0YC6+FAgEGDZsmK6mbcSNRNjx8su4kUi+u1JwcrHY0lnefl9suc518/TMqUWjUTZs2MDQ225TTTGg+m1PNTy9H7D7/IRLu3jffYDb8D6Q3ElmNXwr8CfgS3jXNGhvJXA8u0/t9F6sLV7Da2Jt13exr8VMNcWW6ok9ZW5LedtSDe9Y1g/Oam3lC53Mbx2clT2qJ/6mBXPxpfgfVjESibD9pZfy3YuClIvFlo7y1mJL9qme2FLeeaAanlZ33p476X7RQd7/i/fm8dA0dw3jXRg4rhTvgsIATcDvgc8BfbvY12KmmmJM9cSeMrelvE2phncs6wdnuW6n89vvB2cVFNUTX9PHeOJLkUiElStXEtEndSackhIGXnQRTklJvrtScLq72JJqwaWjvPdmsSUc+38ttqSmemJLedtTDbeVLu/XgfeBz3dw3/3x3tB+DNQBb8baABYA1cCELPfX71RTOrYQeBj4PjAT+G0X7rsL+APwM2BW7P7PBQIp5/dvY79v/29Ou8dsxVtMmYV30MHzeEczJvsYuBl4twt9LWaq4ellfX6TOm/N79xRDe/YD4BvAl8Agl28b/zgrK8DZ8Qb09TwuPjBWZ9P85jJB2cdCYyPtcXV4B2c9bku9rVYqX77m44wF19yXZf6+nrcAju1Q9FyHMpGjgQn7fF0kk1p8o4vtlzXwV3jiy0HA814iy3Hx36nxZbUVE9sKe88UA23lSLveryFmTOB8g7uOgn4JXBH7OfReDW8Fu9bTDNy0V+fU03pWC5OHZeunpQAV6ZoS/YixX10Yk6ubZOmhkeAvwNLgM14b+yHAGcBI2K3KfbTJ+TkOhRp8tb8zg3V8I7l4ptwHe0T6ptwWaZ9cF/TgrmIiA9osUVEZO+1P24tQAenVMmBJ/AWZY7q5Hb98Y4k+xjvSLJyvDenTwCfAQbjLXw9h7cYNwJvYaYiB32W4pCTU8el4bD7GxHpFPup46wuJBwFfoV3juGT8WpBC7A+9t+4FynuBVzNb5Hs0cFZIm1pwVxEJANabBERCzk5OjHFbWd28DiD2f1mvhiOTtwKzG7XdhUwyuj5lwOrgW/gbaNkEbwjskrZ/VVrB29BK+6FWNvJwEZgPnApMBJvoWs+3tetRVLJyXn6u6HYj060WsD9B7AKr64kL+KOaXe7Yl/A1fwWyQ4dnCWyJy2Yiy8FAgFGjhypq2kbccNh6hcswA2HO79xEbJebGmftxZbckv1xJby7lgujk5MVcOnpbj/e8AzwCFJbS/i/6MTBwD/1a5tcA6fr33edXhHg/53itu+Hvt3KW1zj9uIV8OvwKvx7+KdcmF07PcnAvfgHeVVlr0h+IpqirFoNO0+YStwK95i7wC8OnQa0CfpNsV+dGIuFnBT1fCX8fbjOjviWQu4XZfufY/md26ohnvMDs5KU8N1cFZu9PR1FL/Tgrn4UiAQoKqqKt/d6DmiURpravLdi7yxXmxpn7cWW3JL9cSW8u5YTo5OTFHDUy2yvBa775FJbcVwdGIIGG75hO3ynkDqD1h/ireocgLeIkoqT8TuPyL2s0vb0y20JLX3VKop9lLtEw6N/YvP5Vq8DwD/hbcPFd/H0NGJe6FdTdmGt1j1KbyL8y3C+8B0MN7BEckLslrA3Qsp/mZqfueOarj9wVnt57cOzsqhHr6O4ndaMBdfikQirFixgkMOOYRgsKvXi5auckpKqJwyhc0PP4zb2prv7pizXmxpn7cWW3JL9cSW8u5YLo5OzKSG78J7wzQSqExqL/ajE9fgfbgA3geT2/ByAC+LfrH/fx7vdDdX0LYer8Kro82xnz8ClgeDDPj0p/nECy9QEg4zkLZvLJOVk/4N8SK8C/ldltR2IN4pcv4MHIB3BNcIoFcHYyx2qikes6MTAwEGT526Rz05sd3N/gNvgfFRvA/247/X0Yld176G18fa38DL7/N4NWAR8P/w5kL8KNFiWcC1PDViqr+Zmt+5oxpufHBWihqug7Nyp6evo/idFszFl1zXpampSVfTtuI4hCordXXnFHKx2PKm67LPpk1UAyWgxZYcUz2xpbzzIIMa/iZeLTqyXXuxH534HN7CUdya2D/wju6K13AXr8a39yTewkjccmB5JALPP8+3S0rYZy/7tQMv3y8AvZPah+Cd0/45vFMy7Aecv5fPUSxUU+yPTsx0n/AQvA/Z3mvXXgxHJ5pe26ZdDY/P9FbgckjUmf8A7sPb54wvmBfDAq75dSgyfN9TzPPbkmq4/cFZ7ee3Ds7KIa2j+JoWzEVEuiEniy3hMDz5pBZbRMTMIrxacWi79mI5OjGdTBcpJsX+tfftFG1OaSnVM2dSN3duh28Q53bwu35458pNZVzsn0ic+anjusCl44VkPx6dmO8LCcfPmV0FbfYTHeAg4P/w9gP7JbX7eQFX81t6slwcnLVo5Uo+jkYZjfehjw7OEklNC+YiIt2gxRYR2RumRyd2og7vCLlj8b7VkqwYjk4UKXbm5+nP0D/xjoLer4Pb+PHoxHwv4A5iz1odF88p3d8TPy7gan5LT5b1g7OiUZY/+SQAN5F+obwzOjhLegItmIsvBYNBRo8e3WPPc2bNDYfZOn++ru5sRHnbUj2xpbztj07srKbELw7a/nQscX4/OtGaargt1ZSOZf3oxGiUvz70EOHW1sTRiR8DjwOHs/saCLV4iyVD2H16kPb8enSi9QJu+5oSxDtNwj/x/p7E67OLd+G+QaS/rkSxLeDm5NSIra289dBDfLK1tUfOb2uq4R3L+sFZgQBlI0bQ/O9/QzTVEvtuOjir+7RP6G9aMBdfchyHioqKfHej54hGaa6t7fx2kh3K25TqiS3lnYejEzuoKWG8U6sMAz6RwUMV4tGJ1/Xg856KakpncnLquI0bgd1HJ5bhfRvlJbyjDqN4pwo5Hvg0bS8cHFesRyfm5ELC0SjU1iY+oAA4DW9x/Od4i2RleAu0HwJfTtO3YlzAzcn8dl3YuFHz24hquDG9z7SlvH1NC+biS+FwmGXLljFu3DhCIU3jXHNKSxkyfTof3XcfbktL53fwmUJbbNH8tqW8bSlv+6MTO6rhb+Mt7qQ6KimVYjs6MRc0x20p745l++jEVPWkD963TLqiWI9OzMkCbuz/v11SQmlrK+Ad6Xw18Efgf/FO8zUUbzH8Uyket1gXcHN1asTkOa75nVuq4baK/X19oVHe/qaKJL4VibQ/A6zkklOa6vgJyRXNb1vK25byTi8nXy+PRtlnzRqqXXeP894uwjsXbiZv3ovx6MRc0Ry3pbxtaZ8wPctr21QDUzJ8Pi3gdo3muK1iruGFeGDWkiVLuPCOO/QBhRHVE//SK0SkyCwENgAf4F2A7Qi8I0cy8S+8r+b/G2+Rpjfe1/QnRaNUJ90uincUyjt4izGNeF9NPBg4hbZHrrTiLaYsxys4RwGn0vZiRB8Dd+EtwhyYYV9FRIpRTo5ODIfhySf5dkkJ+yT9bhteHR9P27qdSrEenSgiIiIiItKeFsxFisxLwL54X8dc3MX7vop3ZOPxeIsgO4G/AT8Oh7nh3/9mUOx2rXhHNx6OtwDeF1gP/BXv6/3TIXEU44vACuA8YBfwNN75AI9Iet4ngUPRYrmIiOXRiRXAnAyfT0cniuRHoR6d+KVZs3R0omSF5riIiBQix3UL7C9UAWpoaKC8vJz6+noGDBiQ7+4I4LouTU1N9O7dG8dxOr+Dz9zVjTFFgUDs/2/GW4jO9AjzHew+ejGuGfghsG9JCVeEw+C6RIEmvIXyZMuBR2PPF18Q/zFwCN5FccA7z+Iudl+QqAZ4Crg+xeN1ptB2sLOl2Od3oVHetnpC3t2p4TnhOIQGDSK8ZQsUUN1UDZdsUN62lLc9ZW5LedtS3rZ6Qt4FtR+uffCC05X13UCHvxUpYKU6F1RK3XlRt18sB+/K8EOAba2tiSIfIPXidvwidvVJbWHaXj2+NNYG3qL774HPpXm8nkzz25bytqW8jbkukYaGgtpRL3aa47aUty3lbU+Z21LetpS3LeVtSPvgvqbvGIkvRSIRlixZwoQJE/RVuRxrwjsf+iEHHYSzdm2HV3f+V+y/Q5La9se7UNzBeEerv4l3yhfwzodbDUzIcp/9TvPblvK21RPyLrSjNuJfLy/mzAtJT5jjhUR521Le9pS5LeVtS3nbUt622pwWsYN1FClMOsJcRDr0FNACnHPccR3erh74I95FQscktU/C+2TuDuBHwFC8BfNavAuMnpf1HouIiIiIiIiIiOwdXy2Y33777Rx77LH06dOHioqKTm/f2trKDTfcwKGHHkrfvn0ZOnQoX/nKV9iwYUPuOyuSY5F2/3JxPONCYBlwdjDIAfvum/Z2jcAvYv//ZdoWlv7AN4GbgO8AlwMO8ATwGWAw3rnP7wJuAR4GtmVxDCIiIiIiIiIiIpny1YJ5S0sLF1xwAV//+tczun1jYyNLly7lu9/9LkuXLuXJJ5/knXfe4eyzz85xT0VyaytwY7t/tVl+jueAvwKnA8cHg2lv1wj8DO8I86nAoBS3cYCBQHns5xdibScDG4H5wFl4C+p9Yj+LiIiIiIiIiIhYc1y3wE60mYFHHnmEa665hm3btnX5vosXL+bII49k3bp17LfffhndpytXURUbrusSiUQIBoNFeXXnzq7sHAY+bNc2GOjVru1m4FDgwi4+/3PA88Bn8U6pAt75t9qfdyu+WL4VuBL4RAaPvRG4F7gCGAH8A1gMXBP7/QbgHuAHeBcc7UihnSc4W4p9fhca5W1LedtT5raUty3lbUt521PmtpS3LeVtqyfk3dlairVU6yj5VqzrKJnoyvpujzvLf319PY7jdHhKl+bmZpqbmxM/NzQ0AN5Fs8LhMACBQIBAIEA0GiUajSZuG2+PRCIkfxaRrj1eqOKPm9wO3kUZMmkPhUKJ4hfnOA7BYHCPPqZr99uYmpqa6NWrF47jFM2Y4n10SkvBdXFbWyEYxEk6wtuNRgmFwwwPhXACu78k4kYiEInglJRA/I9EUmFu0w644TBEo95zJXmupYXngVMDAU6LXwjEcQj2709461acWFuj6/KzcJitrstUx2FYScnuB4n3PRBI3D7e/kRrKxMchwPit49EaInnEQrRChAOQ0kJRKN7jimp7/neTh21d2fuRaNRdu3aRa9evQiFQkUxpkLeTo7jsGvXLsrKyhI7jn4fUyFvJ9d12bVrF/369SuaMXXU90IYUzzzeE0phjFl0p6vMTmO02YfpRjGVMjbyXVdmpub6du3b9GMKbmPhTamcDicqCeBQKAoxlTo28l13ZQ1xc9jStX3QhlTck0pljF11J7vMUUikURNCQaDRTGmQt5OwWCQ5uZmSktLE/XE72Nqv53arKWkWI9It8ZCOMxCx2ED8IHr0gAc4Thc6Lrp11KS2t+NRlkaDvNvvFPM9gaGBwKcPmAA1Vu3tll7ua6DBfTBwPVecISDQZ6NRHgzGiUEHAWc6jgEktZetkaj3BUOc1kgwH8kjTU+JlKtG0HetxPkZ+61H0dHetSC+a5du7jxxhu5+OKLO/wkYfbs2dx66617tC9btoy+ffsCMHjwYEaNGsXatWvZtGlT4jbDhg1j2LBhvPPOO9TX1yfaR44cSVVVFStWrKCpqSnRPnr0aCoqKli2bFmbiTN27FhKS0tZsmRJmz5MmDCBlpYWli9fnmgLBoNMnDiR+vp6Vq1alWjv3bs3hx12GJs3b6a2dvcJO8rLyxkzZgwbNmxg/fr1iXY/jWnjxo3U1NRQUVGB4zhFMabk7VQ9cybNtbVsnT+f/scdR78TTkjcvrGmhvoFCyg/7TT6HH54on3Hyy+z/aWX2Hjiiewq905+4j7zDA29erF8xw4GnHwyBx98MANic/jxn/yEP3/8MTeedx6fGjUKgD++9hrP/fWvHOQ4HP+Vr7Aj/uCxPwIVCxdSOWUKLa2t/OTRR9nw4YecDQT33ZcdkyYl+tJn1y6c3/6WPmPHUn7mmYn2vzz/PJsXLeKbxxzD4FNOAeCYTZt45qGH+LPrcsj48fz5gw/4D8dhxKWXJsa0z/nnUzZyZOJx6hcsoLGmJu/bCXLzempsbGTbtm1UVFQwZsyYohhTIW+n/fffn0WLFtG7d+/EjqPfx1TI28l1XbZv386nP/1ptm7dWhRjgsLeTq7rJmrKxIkTi2JMhbydBg4cyKuvvkr//v0TNcXvYyrk7RRfTDzxxBNZt25dUYwJCnc7rVy5MlFP+vTpUxRjKvTtFAwG+fvf/55431MMYyrk7eS6Li0tLRx33HG89dZbRTEmKNzt9P777ydqSlVVVVGMqZC307hx41i6dGnioKxiGFP77VQ9cybhzZvZNG/eHusRna2xvBwIsF91NROHDOFvb75JSWUlfPRR2vWIyilTCFVWAvA/TzzBtn//m+N37WLMhReyo6WFP77+OvfW1XFlRQUjd+ygeuZMAGZ98AEAWx57jEDfvuxz7rn864MPeOz55znqqKPg9dcpGzGC14YN46033uDy005jx5YtPPq3v7Hv8OF8dvLkRF9+9fDDHPrhh4w//viM142AvG8nyM/c27lzJ5nK+ylZZs2alXJxOtnixYuZMGFC4ue9OSVLa2srF1xwAe+99x4vvvhihwvmqY4wHz58OFu2bEncr9A/VYPi+6Qwue8tLS288cYbjB8/PvF8fh9Tch/v69u3008/U35SGInwoONQm+ZlfVUoxCdj9/lz7Ejy5LaftLamvS/AXWVl4LpsdV3uaG1Ne7sJjsOXXLfNJ7o7XJc5ra18ARjbbkxLw2Gei0bZDuzvOHwhFGKQ46Q+ap7dn+jOCIeL8vUUDodZunQp48ePT3z67/cxFfJ2ikajLF68OFFPimFMhbydIpEIS5cuZeLEiYl++n1MHfW9EMYUzzxeU4phTJm052tMqWqK38dUyNupo5ri1zEl97HQtlNLS0uinoRCoaIYU6Fvp3A4zJIlS/aoKX4eU6q+F8qYkmuK4zhFMaaO2vM9ptbW1kRNKSkpKYoxFfJ2AvbYR/H7mNpvpzZrKYGuHWEeDQYTz/PtlhYO7cIR5ttdl36RSJsjyVtKSrgTqGps5ArX3ePb/W5LCzgOTkkJvw2HeSMa5YaSEgbF+v5j1+WQQIDPBIPguvyutZVdwOTY4yyLRHgqEuF6oG8X1o2uC4fzvp0gP3OvoaGBQYMG+eOULNOmTePCCzs+w/KIESO69Rytra188YtfZO3atfzf//1fp6GUlZVRVrbn2ZNDoVDiq8xx8Q3QXjBpombS3v5x96bdcZyU7en62NX2QhtT/EWU/Hu/jynexzbnuIpEEl+baSMcJtXS9tc7+gws6T6TYv+S29JdTtcpLaV65kzq5s7FbWlhH2Bu+meBeB+i0cRY+gKJj8bajWlc7F/ivq2tbcbmplmcz/d26k57R3PPdd3E/I5/8u/3MWXax662Z2NM0Wg0ZT3pqO+FPqaO2gthTPF5XUxj6qyP+R5TJjXFb2PKpD0fY+qopvh1TB21F8KYOqspfhxTZ+35GlO8jgSTFhX8PqZC306O43SppvhhTF1ttx5T+1PfdKfv6dq1nbz25JoSv43fx1TI2ykcDqetJ34dE7TdTm3WUpLWI9pIs8YSaN8eP1VTmvWI5PZ+ye2x5ywFPrHvvmxat27PviU9R1NLC8uBkeAtlsf6HgZKk/pUCuyIPU4T8Hvgc3hrLV1dN8r3dkpmOffS9TeVvC+YV1ZWUhn7CkMuxBfL3333XV544QUGDRqUs+cSW+leDJIbhXahimKn+W1LedtS3vaUuS3lbUt521Le9pS5LeVtS3nbUt52mlyXf9fVMdJxdh9UmMKbQAtwZLv2/YFFwMFAc+x2x8d+twCoBiYguZL3U7J0xXvvvcfWrVt55plnmDt3Li/Hzr3zyU9+kn79vM9zRo8ezezZszn33HMJh8N84QtfYOnSpfzhD39gyJAhiccaOHBg4mvJnenKVVRFsqHQruxcqHry1Z1FREREREREZLdsraXcDBwKdHw+jI79Bm+R+5vAsA5u99/AJuC7QElS+3bgl8D7sZ9HA1+J/fxzYAbeRUK7qievo3RlfTfvR5h3xS233MKvfvWrxM/jxnkncXjhhRc4+eSTAVi9enXiJO/r16/nmWeeAeDwpJPct7+P+I/rutTX11NeXp74ylwxKbQCVux5FxrlbUt521Le9pS5LeVtS3nbUt72lLkt5W1LedtS3p72Jy4JANlOYyGwDDh/0CCGffwxJJ1zO1kd8B5wLG0XywH64y22fwwEgXIgDDwBfAZvsXw58BzQAIwAzgMqsjqSnmvPk74UsEceeQTXdff4l7zw7boul112GeCd+zzV7dvfR/wnEomwatWqlBexkOxT3raUty3lbUt521PmtpS3LeVtS3nbU+a2lLct5W1LecNW4MZ2/2qz/BzPAX8F/jMY5PNXXdX2wqPtLIr9t/3pWOIcYCDeYjnAC7G2k4GNwHzgLOA7QJ/Yz5IdvjrCXERERERERERERKSrBgD/1a5tb05rks5zwPPAZ4HPdHK++DCwFO90LZ/I4LE34i2YX4F3xPm7wBC8U7UAnAjcg3e+87Kud13a0YK5iIiIiIiIiIiIFLUQMDxHj/187N9ngEkZ3P5tYGeGtwXvVCwT8E69AuDiXSw0riWpXbpPC+biS47j0Lt37x593i1LytuW8ralvG0pb3vK3JbytqW8bSlve8rclvK2pbxtKe+OrcFbwAaIAtvwzhEOMBLoF/v/54G/4B3pPSrW9je8o8sPAsYA6wAnEmHbW29RH4mwX4rnW4R33vJxGfRtEbAZuCyp7UDgWeDPwAGx5x8B9Mrg8aRzjusW2NUFC1BXrqIqIiIiIiIiIiIitu7qxocBD5L+fOZXsXtxPH7aleS2ju4LMLfdz9uAO4DxwIWd9GtH7P5fAMa2+92yWH+2A/sB5+Od87wj1/XgZeCurO9qwTwDWjAvPNFolM2bN1NZWUkg4Ktr1/qS8ralvG0pb1vK254yt6W8bSlvW8rbnjK3pbxtKW9bPSHv7iyYZ10gQJ+xY2lcvhyi0Xz3JkEL5pmt7xbnK0SKXjQapba2lmgBFZ1iprxtKW9bytuW8ranzG0pb1vK25bytqfMbSlvW8rblvK25YRClJ95Jk5IZ8P2Iy2Yi4iIiIiIiIiIiIigi36KiIiIiIiIiIiIzxXS6UbC4TBLlizhS7NmEdJR5r6jI8zFlxzHoby8XFd3NqK8bSlvW8rblvK2p8xtKW9bytuW8ranzG0pb1vK25bytqW8/U0X/cyALvopIiIiIiIiIiIi4k+66KcUvWg0yvr163WxCiPK25bytqW8bSlve8rclvK2pbxtKW97ytyW8ralvG0pb1vK29+0YC6+pMJjS3nbUt62lLct5W1PmdtS3raUty3lbU+Z21LetpS3LeVtS3n7mxbMRURERERERERERETQgrmIiIiIiIiIiIiICKAFc/GpQCDA4MGDCQQ0hS0ob1vK25bytqW87SlzW8rblvK2pbztKXNbytuW8ralvG0pb39zXNd1892JQteVq6iKiIiIiIiIiIiISOHoyvquPuYQX4pGo6xZs0YXTzCivG0pb1vK25bytqfMbSlvW8rblvK2p8xtKW9bytuW8ralvP1NC+biS9FolE2bNqnwGFHetpS3LeVtS3nbU+a2lLct5W1LedtT5raUty3lbUt521Le/qYFcxERERERERERERERIJTvDvhB/DTvDQ0Nee6JxIXDYXbu3ElDQwOhkKZxrilvW8rblvK2pbztKXNbytuW8ralvO0pc1vK25bytqW8bSnvwhNf183kcp7aYhnYvn07AMOHD89zT0RERERERERERERkb2zfvp3y8vIOb+O4mSyr93DRaJQNGzbQv39/HMfJd3cE71Oh4cOH8/7773d6ZVvpPuVtS3nbUt62lLc9ZW5LedtS3raUtz1lbkt521LetpS3LeVdeFzXZfv27QwdOpRAoOOzlOsI8wwEAgGGDRuW725ICgMGDFDhMaS8bSlvW8rblvK2p8xtKW9bytuW8ranzG0pb1vK25bytqW8C0tnR5bH6aKfIiIiIiIiIiIiIiJowVxEREREREREREREBNCCufhUWVkZ3/ve9ygrK8t3V3oE5W1LedtS3raUtz1lbkt521LetpS3PWVuS3nbUt62lLct5e1vuuiniIiIiIiIiIiIiAg6wlxEREREREREREREBNCCuYiIiIiIiIiIiIgIoAVzERERERERERERERFAC+YiIiIiIiIiIiIiGdHlIIufFsxFRERERER6sJ07d+a7Cz2SFlxsrFmzJt9dEMmZxsbGfHehR3niiScAcBxHNbzIacFcREREREQKSjQazXcXeoyvf/3r3H///Xz88cf57kqP8aMf/YhVq1ZpwcXArbfeype//GXeeOONfHelx2htbc13F3qM8847j1tvvZX6+vp8d6VHmD9/PlOnTuWOO+4AtGhe7LRgLgVJRUdE9pYWWfJL9Tu30s3vSCRi3JOeQ3Pa3gUXXMA111yjeW1kx44dPPTQQ/zmN7/RormBF154gYcffphZs2bxr3/9SwsuOfbJT36SAQMGcOutt7J48eJ8d6foXXTRRUyePJldu3bluys9wtFHH81dd93FAw88wLZt2/LdnaJ3zDHHMG3aNB577DG+//3vA1o0L2ZaMJeC0H4BwHEcQG9ScyXdgovyzo1UuSrr3IhGowQC3p+2n//859x888186Utf4qWXXmLTpk157l1xSle/JfuS5/fTTz/N/fffz9y5c9mwYQPBYDDPvStO0Wg0MadbWlrYvn17nnvUM0yaNImf/vSnzJo1i3A4nO/uFK34vsijjz7KmWeeyb333svjjz/O1q1b89yz4nbKKadw/fXX8+GHH/Ltb3+bd999VwsuOXTJJZcwbdo0gsEgt912GzU1NfnuUlH70pe+xB/+8AeuvfZaLZrnWCQS4frrr+f+++/nO9/5Dj/96U/ZsmVLvrtVtMLhMCNGjGD69OlccsklzJ8/n3vvvRfQonmxCuW7AyLJCwC///3vWbNmDSUlJZxwwgkcfvjhuK6rBZgsSs77D3/4A/X19Wzbto2pU6dSWlqa594Vn+S8N2zYwM6dOznwwAMTv9f8zq541tdffz2PPvooF154IQBf/OIX+drXvsbNN99M796989nFopI8v3/3u9+xatUqQqEQxxxzDCeffHJ+O1eEkuf3E088wQEHHEBJSQk33HADf/3rXznllFPy3MPikjy/Z8+ezaJFi1i0aBGXX345xx57LKeffnqee1icIpEIU6dOpW/fvlx66aUEg0FuuukmysrK8t21opO8D3LTTTfx9ttv88ADDwAwefJkysvL89m9otTS0kJpaSmXXnopO3bs4Mknn+S73/0uP/zhDxkxYoT2C7MouYYPGDCAyspKnnrqKb797W8ze/ZsDjvssDz3sPhEIhE+//nP88QTT3DeeecRDAaZPXs2/fr1y3fXik40Gk0cLHHJJZewaNEifvCDHxAMBpk6dSoVFRX57WCRcV2XUMhbPv3jH//I+++/z8aNG7nllltobW1l5syZiUVz1fDioSPMJe/iOzIzZ87kmmuuYeHChbz00kuMHz+e5557TgUny5IXXL75zW/y0EMP8dOf/pRPfepT/P3vf9cno1nkum4i7+9973ucddZZnHjiiRx33HH893//N9u2bdP8zoE//elP/O53v+NPf/oT99xzD9OnT2fjxo0cdthhWizPsuR6ct1117F06VJWrVrFpz/9aebPn5/n3hWn3/zmN/z617/mf/7nf/jLX/7ClVdeCdDmiFDV8eyIz++bb76Ze+65h3POOYe5c+fyv//7v9x+++188MEHee5h8YlEIokFgPHjx/PNb36T2267jbvvvltHmudAfI5Pnz6dL33pSwSDQVpaWrjpppt49NFH9fX+LHNdN3Fwyt13383ixYv597//ze9+9ztuvPFG1qxZo6MUsyg+v2fMmMEVV1xB3759OeWUU1i6dCk333yzzmmeZcn1u7q6mm9961s88MAD3HbbbTQ1NeW5d8UneX4fd9xxuK7LgQceyI033qjTs+RA/D37d77zHa699lomTpzI7NmzOeGEE/jZz37G7bffnridangRcUUKwG9/+1t33333dV9//XXXdV330UcfdR3HcX/961/nuWfF6Re/+IVbVVXl1tTUuK7rur///e9dx3HchQsXJm4TjUbz1b2ic/vtt7tDhgxxn3nmGbexsdE97rjj3AMPPNBdvnx5vrtWlObPn++efvrpruu67uOPP+7279/f/clPfuK6ruvu2LHD/ec//+lGIpF8drGoPPXUU+4nPvEJ97XXXnNd18vfcRz34YcfznPPitPs2bPdGTNmuK7rur/73e/cfv36ufPmzXNd13Xr6+vd+vr6fHav6Lz11lvu2LFj3Zdeesl1Xdf9+9//7paWlrqPPPKI67quGw6H89m9ojVz5kz3oIMOci+//HJ33LhxruM47ne+8x23tbU1310rOr/73e/ciooKt6amxt2+fbvruq47ZcoUd8iQIe5///d/u9u2bctzD4vPnDlz3P79+7sLFixwa2pq3O9973vuEUcc4X7xi19016xZ47qu9sOz5dVXX3X33Xdf9+WXX060PfbYY+6JJ57onnHGGdoXz4GZM2e6o0aNcqdNm+Yef/zxbigUcr/+9a+7TU1N+e5a0Xn22WfdAQMGuIsXL078fZwzZ47rOI57++23u1u3bs1zD4vLBx984I4fP77NGtXatWvdmTNnusOHD3fvvvvuPPZOckEL5lIQ7rjjDveqq65yXdd1n3jiCbdfv37uz372M9d1vQWAdevW5bN7ReeWW25xb7rpJtd1vQ8rBgwY4D744IOu63oLinHaWe+eaDTqfvzx/2fvzONqWr8//uwGQ5qUBiokmufSoFFEszJmush4zTORyjxkSKLBPBMSNzPJGDJGInNkSoM0T5/fH73OvmeLq3NCX/2e9z+38+y9z+u56yxrr2c961krFw4ODti7dy8A4OzZs5wAV3l5OQ241IFvBb6XL18OW1tbXLx4EdLS0li/fj17bffu3Zg0aRJ1IH8ia9aswaBBgwB8236npaXV5/T+aL6l3+PGjcNff/2Ff/75h7MZBAARERGYOXMmSktLf+c0GzQPHz6Evr4+gH83KHjvy8LCQsTFxeH9+/f1OcUGx8mTJyElJYWrV68CAPLz8xEREQFRUVHMmzcPZWVl9TzDhsXGjRuhq6uLnJwcjs0ZOHAgZGVlsX79emRlZdXjDBsOVVVVKCkpgYuLC2bNmsW5FhkZCXV1dfj6+uLFixfs/ZS6ce3aNcjIyLBJWTw2b96Mxo0bw8PDg7U1lLpz/vx5SEtLs5vMxcXFiImJQZMmTTB27FgUFhbW8wwbFjExMdDS0kJ2djbHXixYsACNGzfGmjVrqP3+ieTl5UFVVRXLli3jjL969Qp6enqQkZHBnDlz6ml2lF8BLclC+e18q+FkQUEBqaqqIrGxsWTIkCEkJCSEjBw5khBCyD///EMiIiJIQUHB755qgwDfOBJ0584dUlhYSC5dukRGjhxJli1bRsaMGUMAkFWrVpGQkBBCCG3eJwz88mYYhlRUVJCsrCzi4uJCTp48Sby9vUlISAgZNWoUKS4uJjt27CAvXryoxxn/ufDXpjx16hR7tHbQoEHk/fv3xMHBgaxevZqMHTuWEEJISUkJ2bNnD8nPz6d1/YTkW/a7vLycMAxDYmJiatjvU6dOkejoaHosVAj49fvOnTts01oHBwdy79490rdvX7J48WLy999/E0IIyc/PJ8eOHSNVVVW0H4WQfEu/v3z5Qj59+kTWrl3LeV8SQsjdu3fJjh07yLt37373VBs0+fn5pGXLlsTQ0JAQQoiUlBQZM2YMWb58OVm0aBEJCwsjZWVl9TzLPx+evldUVJDc3FwiLi5ORERESFFRESGkuqZ5aWkpCQwMJBcuXKjPqTYYGIYhjRs3JhISEjVKOo0ePZo4ODiQ+Ph4MmLECJKRkUH9cAHh98F5fzdu3JgoKCiQly9fEkL+1Xs/Pz+iqalJUlNTSVxc3O+eaoOloKCANG/enOjr6xNCCGnSpAnp06cP2bBhA4mIiCBLliyha3oh4ek0v56LiYmR58+fs2U+S0tLCSGEeHt7ExERETJ16lRy7Nixepnvn863fEJRUVHSqVMnkpqayrHhrVu3JpaWlkRbW5u8ffuWlmRpQNCAOeW3wh8ASE5OZl+Y2tra5PTp0+Svv/4iS5YsYRej+fn5ZPfu3aSyspI2CxGCqqoq1tlOTU1lAy6DBg0iiYmJpEuXLmTVqlVswKWwsJAkJyfT7tpCwi/vz58/E0IIadGiBREXFye+vr6kX79+ZM2aNax+v3//nuzYsYPcv3+/3ub8pwK++vCzZ88m06ZNIzdv3iR5eXmkefPmZNKkSURDQ4MkJCSQhw8fkmPHjhEfHx+SkZFBoqOjaX05IeC335cvXya5ubmEEEI6dOhAEhMTydChQ8nixYtZ/S4oKCBbt24llZWVdINCQPhlHRAQQIYOHUqSk5MJAOLu7k7at29PFBUVibi4OHn79i25e/cu8fX1JZmZmWTp0qWEEFrHXFD4Zb5lyxYSERFBCCHEwsKCuLm5kSlTppCpU6eScePGEUIIKS4uJkuWLCHl5eVsYJciON/S0xYtWpCnT5+Shw8fEkL+XbR27tyZNGnShMyYMYNs27btd06zQfD14p/nrwwbNow0bdqU9OnThxBCiISEBCGEkNLSUjJo0CAyZcoU4u3t/Vvn2lD4VsCFkOp1z+XLl2v4f5qamsTIyIhYWloSVVXV3zHFBgO/D15YWMj6KMbGxsTGxoZMmjSJ3Lhxg7Xz79+/J9ra2iQoKIh9b1IE41v2W1VVlbx584Zcv36dc4+lpSWRlZUlS5YsYd+vlNrDr98VFRWsXH18fIidnR3p378/yczMZJtjS0hIkIkTJ5KdO3eSgQMH1tu8/1T4fcJHjx6RW7dukezsbCIpKUlGjhxJ4uLiyPr169mkt8LCQvL582cycuRIsmXLFrrObEj89px2yv9b+I95BgQEQFNTE0ePHmXHBg4ciMaNG2P79u14+PAh7t27h+7du8PU1JStyUWPJtYefnnPnTsX9vb2OHbsGKqqqvDkyRO4ubnBwMAAO3fuRHl5OR48eAA3NzeYmZnRGqFCwC/vNWvWYOrUqWwpij179kBVVRXdu3dn7ykoKICbmxucnJxoSZY6sGDBAigoKODixYucY/qlpaXYvXs3tLW10bx5c5iYmMDb25u9h8pcMPht79y5c9GuXTscPHiQ1ftJkyaBYRiEh4fjxo0bSE5ORrdu3WBsbEztdx0ICAiAsrIyTpw4wSkjVFBQgEGDBkFPTw9iYmKwsLBA586dqX4LCb9uTp8+HWpqaggNDWXLwd27dw+urq6QlJTEihUrEBgYiK5du0JPT4+VOe2LIDj8MuOVEeLZCw8PD3Tt2hW3b99m73n58iXGjh2L+Ph46qcICL+so6KiMHToUPTr1w8hISEAgDNnzkBNTQ0ODg64ePEiLl68CBcXFwwZMoR9jtoVweCX+alTpxAbG4tdu3axYzY2NtDV1cWVK1eQlZWFkpISeHt7IywsjLVJ1K7UDn4bvnTpUjg6OkJDQwPe3t64efMmKisr4e7uDiUlJfj7+yMsLAydO3eGo6MjK2Mqa8Hgl1dhYSEqKipQUlICAOjfvz9sbW2RmJjI3vP27VuMGTMG58+fp/ZbQPhlvW7dOvTr1w89evTAtGnTAABXr15F586doa2tjX/++Qfx8fHo3r07288JAJW5AHy95tHS0kLbtm3Rrl07zJo1C2VlZdi7dy+UlJTg4OAADw8PdOzYEQYGBux7kq55Gg40YE757cydOxdKSko4ffo0Pn78yLk2ePBg6OrqQlRUFFZWVnB0dKQBgDoyZ84cKCkp4Z9//uEEXFJTU9G3b1+oqKhATk4OxsbGsLe3p/KuI9OnT4eioiL27t2Lly9fAgDevXuH4OBgyMvLw9HREX369IGdnR0MDQ2pvOtAZmYmLCwscPDgQfZzYmIixo4di8jISPa+e/fu4cOHD6zzQp1G4Zk3bx6UlJSQkJCA7OxszrUpU6bAwMCAtd9du3al+l0HHj9+DG1tbcTHxwMAcnNzkZaWhrCwMLYW67t373D8+HGkpaWxCyqq38Kzbt06KCoq4saNGzWuffr0CbNnz4a5uTlcXV0xceJEVtZU5oLDHwBYu3Yt+vfvj+7du2Pu3Ln4/Pkzrl69ChcXF5iYmGDXrl04duwYunfvji5dulBbXgdmzpyJVq1aYfr06QgJCQHDMJgxYwa+fPmC69evw8rKCsrKylBTU4O1tTWtF/8TmDVrFlq3bg07OzsoKiqiU6dOuHHjBgoLC2Frawt1dXWoq6tDT08PHTp0oJvMdWDevHlQVlbG5s2bcf/+fbRo0QK2trbsenPGjBno0qULDA0N4eXlRTc8hYRfXitXrkSvXr3QqVMnTJ48GW/evMGDBw/Qo0cP6OnpYe3atThw4AC6desGe3t7ar/rwKxZs6CoqIhly5YhLCwMEhIS6NGjByoqKnD16lX0798f0tLS6NChA+zs7Kj9riMhISFQUlLC2bNnAQC+vr5o0aIFrl27BgC4ePEili9fjgEDBmD69Ol0zdNAoQFzym/l2bNn0NfXxz///AMAyMnJQVpaGkJCQpCcnAygOoPo7NmzNADwE7h16xbatWuHCxcuAKhunJWeno5du3bh0aNHAKqDMvv27UNycjKVdx3ZvXs3VFVVcevWLXastLSUbQh34cIF+Pr6Yvz48Vi+fDkNttSRwsJCWFlZYcqUKTh37hz69u0Lc3NzODo6gmEYBAUFAeAuOumiSHhev34NExMTHDp0CACQlZWFlJQUBAYG4ty5cwCA9+/fIykpCc+fP6f2pI6kpqZCU1MTZ8+eRUJCAsaMGQNDQ0OoqqpCX18f+/fvr/EM1W/hKS8vx8CBAzF37lwAwKNHj7Bz507Y2trC2dkZ9+7dA1Dd8Onr5yjCM2vWLCgoKGDt2rVYunQp1NXV0blzZwDVWc+jR49G48aNoaOjA1tbW3ZBSoOJgnPlyhVoaGiwzfhOnjyJRo0asU2aeaSkpFAf/CcRHR0NZWVl3LlzBwCwY8cOMAyD06dPs/ccPHgQ69atQ1hYGCtrGnARnBcvXsDY2BgnTpwAAFy6dAkSEhI19LuoqAifP3+mgdufwOzZs9GiRQts3LgR4eHh0NHRgZGREYBqezNr1izIyMjA0NCQkwRH7bfg3LlzB7q6urh06RIA4MiRI5CSksK6des49z158gSZmZnUfteBqqoqlJWVwdPTExs2bAAAxMfHQ1pamk3I4p2K+xoq74YHDZhTfisPHjyAgoICLl26hMTERIwZMwZGRkZQVFSEvr4+p0QLDxoAEJ579+7B0NAQiYmJuH79OsaPHw8tLS2oq6ujbdu2HIedB5W38CxZsgRdunQBUB1sCQ0NhY6ODpSUlLBkyZJvPkMXRbXjW3pZUFCAuXPnwsLCAmJiYpg2bRqbBTB8+HD8/fffv3uaDZonT55AXl4eR48exblz5zB8+HCYmppCVVUV2tra2LNnT41nqD2pHd9bPJqZmUFTUxNiYmKYMGEC4uPj8eHDBxgZGdVYJFHqzsSJE9G+fXusXbsWtra2cHV1xeTJk2FjYwN9fX1UVlZyfiu66K8bd+7cgZ6eHq5cuQIAOHr0KKSkpBAREcG579WrV3j79i0NANSRw4cPw8bGBgAQGxsLSUlJdvGfl5eHM2fO1HiG+ih1Y/r06Zg1axYAYO/evZCRkWEDMPn5+d98hspcONLS0qCtrQ2gOpgoKSnJ2pIvX75g9+7dbMkQHtRHEZ7U1FQYGRmxAdz4+Phv2u+PHz/i06dPdIOijpw8eRJaWloAgLi4OI79/vz5M/XB68jX/lx+fj6MjY3x6NEjJCYmcuRdUlKC8PBw3Lx5sz6mSvnN0KaflF8GvtHoQE9PjxgaGpI+ffoQV1dX0qhRI7J48WLy5s0bUlFRQR4/flzjGV7DBcp/8y15y8rKkqqqKjJ79mxia2tLKisrybJly8jx48eJrKwsefv2bY1nqLwFh9fUSVlZmbx584b069eP9OzZk1y/fp34+voSf39/EhAQQB4+fFijw7moqGi9zftPgb/xyq1bt8i1a9dIWloaadasGZkzZw45ePAguXfvHlm5ciXp0qULIYSQ9PR0Ii8vX5/T/qP5lj1p3749cXZ2JsOGDSMeHh5EWlqaLF68mLx+/ZpISkpS+y0k/I2cnj17RjIyMkhGRgYhpLo59rJly8ilS5dIWFgYcXd3J4qKirQJdh35XiO+3r17ExsbG7J8+XLi5uZGFi9eTNasWUMmTpxIWrZsSYqLi9nfihDC+ZsiONnZ2aS0tJR06tSJxMXFkQEDBpAVK1aQMWPGkIKCArJv3z5SXFxMWrduTVq2bElERERIVVUVERMTq++p/8/Dr+OVlZWEEEIkJSUJwzBk/fr1ZMiQISQkJISMHj2aEELIzZs3yYYNG8jLly8530N9lNrztV2pqKggt2/fJrKysuTWrVtk5MiRZNmyZeTvv/8mVVVVJDw8nGzfvr3G91CZ/xh+WRcWFhJCCJGTkyMVFRVkwoQJZPDgwWTlypVsE/IXL16QqKgocvPmTc73UB9FePLy8khubi6xtbUlR44cIb6+vhz7vWPHDpKXl0cUFBSIvLw8YRiG2u9awq/fFRUVhBBCFBQUSJs2bUhYWBgZNGgQWblyJWu/U1NTydGjR0laWhrne6h+1w5+P5z3DpSSkiKqqqqkV69exMPDg4SHh7Pyzs3NJQcOHCD37t2rrylTfiPUYlF+CfwBrufPnxMApFmzZkRZWZmcPXuWHDp0iKiqqhJLS0v2GSUlJdKkSZP6mvIfDb+8X79+TZo2bUpERERI69atyaFDh8jdu3eJgoICsbOzI2JiYgQA+1+K4PDLmx9nZ2eSm5tLEhISyJQpU0jnzp2JhoYGuXHjBrG0tCTS0tLsC5kGWmoHAFbWAQEBZNeuXaRRo0bk1atXZOnSpWTIkCFETU2NEEJIQUEBefLkCfH39yefP38mQUFB9Tn1PxZ+/X78+DEpKysjsrKyRE1Njezdu5ecOnWKKCgoEFNTU/YZKSkpGsQVAn5Zz58/nxw9epR8+PCB6OjokCFDhpBBgwYRHx8fQkh1UCA7O5tdjPICARTB4Jd5bGws+fDhAykvLyfDhw8ndnZ2xM7Ojnz69Im0aNGCfWbTpk1EXl6eNGvWrL6m/cfDL3cAhGEYIiEhQdq0aUM2bdpEpk6dygkA3L17l5w6dYoYGRkRHR0d9ntoAODH8Mt6//79REJCgjg4OJDWrVuTsrIyMmXKFBIQEMDakJKSErJmzRoiKytL2rRpU59T/2Phl3lycjJp27YtUVBQIIMGDSKBgYEkICCAbNmyhfz111+EEEKKiorIhQsXSMeOHetz2n8k/LIODw8nb9++JSNGjCDt2rUjPj4+JCoqivTq1Yu1JSUlJWTOnDlEUlKSWFtb1+fU/1j4Zc77W0JCgqirq5P169cTf39/jv1+8OABOXPmDDE2NiaysrLs91D7/WP4Zb1161YiJSVFOnfuTFq2bEkyMzPJ5MmTyaJFi1hZFxcXk4ULFxJJSUmira1dn1P/I+GX96JFi8ilS5fI5MmTiaurK5kyZQqZMmUK0dLSIkOGDCEASH5+PvHz8yOVlZVkyJAh9Tx7ym+hnjLbKQ0Y/uM/gYGBMDc3h4yMDHx8fGoc0yooKMDz58/h5uYGQ0NDekxLCPjlvWjRIpiZmUFXVxdWVlZszVXePUVFRXj79i1cXFxgampKj30KAb+8o6KiMG7cOPj4+CAhIYEd528iVFhYCA8PDzg7O9OjcXVg4cKFUFZWxvnz5wEA48aNQ6NGjRAQEICsrCwAwJ49e9CzZ09069aNNl4REn4dDQgIgLGxMaSkpODm5oZly5Zx7v3y5QvS0tLg7u4OAwMDar/rQGBgIFq0aIFjx47h6tWr6NWrF5o0aYItW7aw94SHh8PU1BQODg5Uv4WE/8jtrFmzoKSkBGdnZygoKMDJyQmnTp1i/w3k5+fj2LFjbIM4WntVePjtSnR0NGJiYlBQUICCggJoaWmBYRiEhISw9xQXF8PV1RW9e/em7806MGPGDLRs2RKbNm1ie6ns3LkTLVu2xODBg7Fjxw4cPHgQXbt25dhwKnPB4LcJ/v7+sLa2RkREBMrLy3H//n306NEDOjo6OHnyJADg6dOncHV1hbm5OX1v1oEZM2ZAUVERW7duxYsXLwAAd+/eRZ8+faChoYGxY8di9uzZ6Ny5M/T19WmDTyHhl9f69euxefNm5OXloaqqCpaWlmAYhlN2kme/vb29qazrwIwZM6CkpITIyEi8e/cOQHVvMklJSXh7e2PVqlXYuXMnnJycqP3+Cfj7+6NFixb4559/8Pz5cwDVvbIiIiKgqakJDQ0NdOnSBZaWljAxMaF++P8jaMCc8ssIDg5mAwDJycnw9vaGsrIyVq5cyd6zc+dOWFhYcBqBUMMjHAEBAVBUVERMTAzOnz8PGxsbtGjRAklJSQCqg7hLliyBvb09bGxsqLzryKxZs9CqVSv4+fnBz88PjRs3RkREBHJzcwFUbwbt2bMHXbp0gbGxMXXU60B6ejo8PDxw+PBhANV1WJs3b45BgwaBYRgEBAQgPz8fhYWFuHTpEq1z+xOYP38+FBQUcOrUKaSnp6Nfv36Ql5dnGyICwP79+2FlZQUnJydqT+rA5cuX0bFjR04jkVF6KQAAt89JREFUPikpKTg7O6NZs2bYvn07gOoNz507d7IypvotPKGhoZwGzfv37wfDMLC3t8eJEydQVVWF27dvY/Lkyejfvz9t0PyTmDFjBpSVlbFmzRo2APDo0SO0atUKXbp0QVhYGDZv3gwnJyfo6+vTAEAd4DWbvHHjRg27vH37dvj4+EBSUhIODg7o06cPteE/gfnz50NeXh7nz5/Hp0+f2PGEhAT0798fkpKSaN26NQwNDakfXkeOHj0KNTU1XL16tca11NRUhIWFwcTEBL169cKUKVOoDf8J8Nvvt2/fAgAyMzOhra0NMzMzLF26FKGhoXBycoKenh5d99SBrVu3QllZGbdu3WLlx/vv9evX4ePjA3V1dTg6OmLw4MGsrKl+C8e9e/c4m5rAvxuhJSUlSE9Ph7+/P4KCghAVFUX98P9n0IA55Zdw5coVGBkZsQGAc+fOoWnTpujevTvU1dURFhYGoDpD8cCBA9Tw1JELFy7AwsICFy5cAFDtSMrKysLQ0BCSkpK4du0agOqsFmro6862bdvQunVrttnHlStXwDAMmjRpgpCQEHz+/Bm5ublYtmwZpk6dSh31OvL+/Xvs2LEDRUVFuHLlClRUVFgbMmLECDRt2hQTJkxAYWEh+wx10IUnOTkZpqambDb/2bNnISEhAS8vL6irq2P+/PkAqhf58fHx1J7UkczMTMybNw9lZWU4ffo0lJSUEBUVhczMTHTs2BFiYmJYu3Yt5xkaYKk9vEUP77+5ubmYNm0aNm7cCAA4ePAgZGVlsXz5cujp6cHY2JhtiP3+/XvaqOwnsW3bNigpKeHOnTs1fpOnT5+ie/fuMDAwgL29PYYOHUoDAHXEz88PI0aM4Ix9Lcs3b96goKCA6vhP4PXr17CyssL+/fvZMf7M86ysLCQlJWHHjh1ITEyk7806EhISAhsbG5SWlrJjX78Xv5YtfW8Kz+7du6GsrIw7d+6wYzz9/vTpE/r27QtLS0s4OTlh9OjRdN1TR6ZNm4Z+/fqhsrKyRsAcqNblz58/c9Y9VNbCc/bsWbRo0QLp6ek1rn3PblB78v8HWsOc8kswMDAgvXv3JmZmZuTs2bNk4MCBZN26dcTd3Z14eHiQ4OBgkpOTQ4KCgkjv3r0JIdVNiWgjEOGQkpIiHh4exN7enpw+fZqMGDGCLF68mHh5eZEuXbqQHj16kL1797I1tQmh8haW4uJiUlJSQubOnUvMzMzI0aNHyeDBg8mePXvIs2fPyLx580jjxo3J6NGjyZQpU0ijRo0IIVTeteX06dPk0qVLpKysjHh5eREbGxuipKREfHx8SNOmTUlMTAxxdHQko0aNIoRUN3kyNzcnt27dIk2bNmW/h9ZJFB4jIyMyYMAAYmJiQhISEsjAgQNJWFgY6du3L3F3dycrVqwgnz59YptQEkL1u7ZcvnyZPHv2jOTl5ZFJkyYRQghp1aoVmT17NhEXFyc7duwggwYNIsOHDyeioqJEW1ublJeXk+PHj5MJEyYQQqr7H9CmcLWnoKCASEpKkoqKCiIuLk6aNWtGfHx8iKamJklNTSVz5swhwcHBZNKkScTAwID06NGDzJgxg0RGRhIrKytCCGH7flCE59GjR8TJyYkYGxuTiooKto8KwzBEQ0ODHDt2jBQWFhJRUVG2VjzvPkrt4TX4TE9PJ8bGxuyYqKgoERMTI2VlZeTmzZtEX1+fqKiosM9RHa8bAMjz5885MuT1qiktLSUMwxArKyvWphBC35vCwKs3XFRURMrLyznXREVFSWVlJYmLiyPGxsbseof/OkU4nj59SqytrYmRkVEN+y0vL0/2799PiouLiYiICGncuDEhhNpvYeA1n7x//z5p1qxZjbrx5eXl5ObNm0RTU5PIy8uzz1H7XXt4ekvIv3IVFxcnkpKSJCsri3To0IFz7eDBg0RcXJz07NmT8z3Unvz/gUYUKHXm0qVLZPPmzWT16tWsoy4lJUVmzpxJJCQkyLZt28jQoUPJkCFDiLKyMtHT0yPt27cnL1684DSdpIandiQnJ5ODBw+SuLg4dszExISMHTuWEEJIZGQk+euvv8jYsWOJsrIy0dTUJADIwoULCSGElTmVd+14//49efXqFSkqKiKEENK0aVPi6OhIXF1dSUZGBpk3bx6ZP38+8fX1JT179iQiIiJk0qRJ5MCBA2ywHACVdy3YtGkT6devH3ny5AnZuXMnmTNnDtutvFmzZqS8vJw8efKEiIuLE3FxcQKAPH78mKxYsYJcuXKFMAxDG9kKSGJiIlm3bh2ZP38+yc3NJYQQIi4uTiZPnkxkZGTInj17yMCBA8lff/1FpKSkiJ6eHjE2NiZFRUWkqqqK/R6q3z9m8+bNpG/fviQiIoLMmjWLuLi4sNckJCRIQUEBuXnzJmnSpAkRFRUlBQUFpLi4mAQGBpITJ07QRsFCcODAATJo0CBibW1NFi9eTDIzM4m4uDixtrYmCgoKJDk5mbRo0YL079+fEEJIfn4+6du3L7G0tOQ046OyrzsPHjwgWVlZhBBCxMTEOAGA5ORkUlJSQqSlpdlgOQ0A1A5+O0xItS0WFRUl9vb2ZO/evSQ9PZ1jnzMzM8mWLVvIkydPOM9RHa89PD+D398oKSkhjRs3Jh8+fCCE/LtxQUi13x4REUEKCgo430Pfmz/ma/3mBRA7duxIkpOTyf79+znXCwsLye7du8nVq1d/2xz/P5Cenk7evHlDGIbh2O+KigqSlJREPn/+TJo2bcoGy6n9rh3f0m+GYUjv3r3JlStXyLFjx9hxQgh5+/YtCQ0NJenp6ZznqP2uHbwNCR68v01NTYmIiAhZtGgRef/+PSGkWualpaVk165d5Nq1a/UyX8r/CL89p53SoNi0aRNatWqFjh07omXLlrC0tOQcGSorK4OJiQkmTpwIoLoGa79+/bB79+4aR3IpP2bz5s1o2bIlTExMwDAMxo8fz7n+6dMntG/fHtu2bQNQXfKmd+/euHLlCpWzEOzcuRNWVlZo0aIFfHx82BJDPJKSkmBoaMg2V71//z7mzp2L7du306NxAhIVFQVRUVHExsYCAN69e4dmzZrhzJkznPtCQ0PBMAy8vLxgaGgIPT09VtZUxwVj48aNUFZWhq2tLdTV1aGlpYX8/Hz2elVVFWxsbDBkyBAAQGlpKfr27Yvt27dT+y0gkZGREBUVxaFDh5CVlYXLly9DTk4Ojx494tw3ffp0tG7dGtOnT4ednR3Mzc3ZY59U1oIRFRUFCQkJLFiwAIMHD4aBgQG2bt0K4F9ZLlu2DHp6erh79y5yc3Ph6emJVatWsd9Bj9wKzvfKYW3duhXt27fHoUOHOOOvXr2Cp6cnLl269Dum16Dgl/WVK1dw7tw5ZGZmAgBevHgBR0dHmJqa4sGDBygtLcX79+/h7u6OTp060bJlQsIvt6ysLE5JkOnTp6Np06Y4d+4cO1ZYWAg3NzcMHTqU2nAB4ZfX3r17ERYWhn379qGgoABAddkKcXFxhIaG4saNG7hz5w66d+8OExMTaruF5Ht24dChQ2jXrh37DuXx9u1buLi44NSpU79hdg0LfllfuHABsbGxePfuHSorK/HixQt4e3vD1tYWhw4dQmVlJZ49ewZPT09YWlpS/a4ja9asQf/+/TFq1CgkJCQAqK5jLi8vDzs7O6xcuRJbt26t0U+F8v8TGjCnCE1kZCTExMTYAMCZM2fQtm1bPHnyhL2nqqoKM2bMgKmpKf7++284OjpyHBnqPNYeXsDlwIEDyM7ORlxcHERFRfHq1SvOfT4+PlBWVsbatWtha2sLCwsLVt50gVR7oqKi0KRJE6xbtw779u2DpqYmRo0axbknPj4eYmJi2L9/P+7evQt3d3f06tWLvU5fsLXj8OHDYBiGs8gEABMTE/j6+sLJyQkjR45k9XjDhg0YNmwYJk+ezMqYOo+CERUVxdrv/Px8JCUlQUNDAw8ePODct3DhQhgZGcHX1xf29vYwNDSk9ltADhw4AIZhOAvK9+/fQ1dXF/PmzcPAgQOxf/9+FBQU4OnTp5g2bRpsbGzQv39/2hROSDZu3AhxcXHExcWxY66urggODkZOTg7ev38PoDqo2KpVK7Rp04ZtxseTOUVw+H2MxMRExMXF4fXr1wCq65R369YNbm5u7Kbb48eP4enpCSsrK6rjdWDatGlQVVVFkyZNYGtry9bnT0pKgoeHBxo3bgwdHR3o6urCzMyMNuP7CSxYsADGxsZwdHTEnDlz2PGhQ4eCYRiMHDkSI0eOhIODA6cBIn1v1g5+OU2bNg0KCgrQ0tKCjo4OPDw82M395cuXQ05ODoqKitDT04ODgwN9bwoJvz04deoU9uzZw/qE7969g7e3N7p06YLQ0FAUFRXh/v378PT05KwzKYIzffp0tGjRAvLy8lBTU0NERATKyspw+/ZtDBs2DBISElBRUYGWlhYsLCyo/RYCflkFBgaiRYsWGDRoEBwdHSElJcVu5L969Qru7u4wNjZGx44d4evrS+0JhQbMKcKxf/9+MAyDI0eOsGPZ2dnQ1NTEzJkz4ePjg0OHDqGwsBCPHj3CtGnT4OjoyAkAUENfe/bt2weGYdimnkC1UTczM0NISAj8/f1ZY//s2TP07dsXHTt2RM+ePam8hSA6OhpNmjRhs52B6iDtgAEDcO3aNVy/fp0dHz16NBiGQdu2bTkLUUrtqKioQGRkJBiGQWhoKDves2dPtGzZEiEhIRgyZAgUFRXh5ub2zeY3dGNCMGJiYsAwDA4ePMiOFRcXo0OHDvj777/h5OSEzZs3IycnB69fv8b8+fPh4eHBacRHHcfakZ+fj169ekFDQwMxMTHsuLe3NxQUFDBy5EgYGBigWbNmnMaepaWltBGfkFy4cAEMwyAqKooz3qlTJxgaGkJNTQ1t2rRhg+kvX77E5s2bsXXrVtqo7CcxY8YMSEtLQ1VVFU2bNkV0dDQAICUlBQMGDICysjLk5eWho6PDCQBQu1I7+IOJCQkJMDExweXLl3H79m0MHDgQVlZWrD2pqqrCgQMHEBUVhX379tFmk0LC73Ns3rwZ8vLyCA8Px7Bhw2BiYoLevXuz18PDw9GvXz/06NED06ZNo3alDrx48QI+Pj5ISUnB58+fsX//flhaWsLR0RF5eXkAgLS0NNy+fRu3bt1ifycqa+GZOXMmJCUloaGhAYZhsGzZMlRUVODly5cYNWoUWrdujWbNmkFHRwfW1tbUfgsIv/0+f/48LCwscOHCBWRlZeHvv/+Gjo4OVqxYgeLiYpSWluLWrVvYtm0bTp48Se13HXn27BkWLlyIq1evAqhu1jxx4kQwDIMDBw4AAEpKSvD582fk5ORQP5wCgAbMKUJQXFwMd3d3aGpqcgIu3t7eaNmyJYYNGwZbW1uIiIhg/fr1AKoNTVVVFTU8QpCdnQ0fHx8oKSnh2rVr7HiPHj0gJyeH/v37o3Xr1lBRUWEXpbznqLwFJz09HQzD1Mgmt7Ozg7q6OqSkpNCqVSt4enqy165cuYJr165RR0ZIiouLsX79eoiIiCA0NBQDBw6Evr4+57TK7Nmz0aJFixolLCiCUV5ejv79+9c4Wuvt7Q0VFRWMHz8eXl5eYBgGixYtAlBzEUT1WzBSUlLw119/wdbWFgcOHICvry8MDQ3x9OlT9h47OzsYGBigsLCQ8yzNRhScN2/eQEdHB506dWIzyXv37g11dXWcOHECu3btgo+PD5o2bYq7d+/WeJ4u+gWHX08vX74MMzMzXLx4EZ8+fYK/vz+kpaWxevVqVFVV4fPnz0hPT8e2bduQkJBA35t1IC4uDiNHjuRkOGdnZ2PUqFGwtLTEypUrv5ksQXVceE6cOIEVK1aw65/i4mLs2rULenp68Pb2Zu8rKiriPEf1W3C2b98OU1NTuLu7s2VYKioqcOTIEVhaWsLBwYENmvNDE4QEg99+Jycnw9LSElevXkVBQQFCQ0MhKSmJefPmoaSkBMXFxXj37h0OHDiApKQkar/rwM6dOzFp0iRMnz6dMz5lyhQ2aP7x48caz1H7LRxHjhwBwzBo37495zTthw8fMHHiRIiIiHAS5XhQP5xCA+YUoXj16hV8fHzg5OSEgwcPolevXjAyMsLz58/Zezw8PKCurs46OTyo4RGcS5cuYcCAATAyMsL169cxaNAg6Onp4fHjxwCqsxiNjIzg7OyM4uJizrNU3oKRm5uLgIAANGrUCHv37gUA9OrVC1paWrh9+zbu37+PVatWQVJSEmvWrKnxPHVkhKO0tBRhYWGQkJCApKQkuwji1QeNiYmBrq4uMjIy6nOaDYKcnBwMHjwYNjY22Lp1K3x8fGrY76FDh6JFixb49OkT51lqT4Tj/v37GDhwIFRVVaGoqMjKlRcgX7RoEWxtbfH58+f6nGaD4e3bt9DT04OlpSXc3NxgaGiIly9fstfPnDkDCQkJNqOI8nMIDQ2Fv79/jQBAQEAAZGRksGbNGmRnZ9d4jr43BScvLw/29vZo2rQpevbsybmWnZ2N0aNHw9bWFvPmzaN2+ydx7do1qKurQ1ZWFidPnmTHi4qKsGvXLhgaGtb4LQD63hSG8vJyrFmzBoaGhlBXV+dcq6iowNGjR9GpUyfo6enV2GimCEdISAgmTJiAcePGccbDwsIgKSmJwMBAvHv3rsZz1H4Lh4uLCxiGgbOzM6cXAlAdNDc0NERAQAD1C38S9+7dw/Dhw9GoUSOcPn0awL+2+ePHj5gyZQoYhqnRr4xCoQFzisDwdu55jZpUVFTQqlUrNpBVUlICoLr2ra2tLaeJHEUw+J3sq1evol+/fmjevDlUVFRYB5H3kp0+fTocHBxqZLVQBKewsBDz5s0DwzDQ19eHubk5J5iYkZGBVq1aYfHixfU4y4ZHYWEhNm7cCDExMYSEhLDjZWVlcHFxQa9evejCs47wFjY5OTno378/VFRU0LJlSzbbmWdP1qxZA0tLS+Tk5NTbXBsaqampGDBgADp27Ijdu3ez42VlZejcuTP8/PzqcXYNj8zMTHTq1AkMw+Dy5csA/tX/tLQ0aGtr4+zZs/U5xQZHv3792ADA18kSgYGBkJeXx8KFC2kAQAi+9e57/fo1evfuDS0trRrN+LKzs9G3b1+MGjWKvjd/Ep8+fcKKFSvQqlUr9O/fn3OtuLgYe/bsgaKiIvz9/etphn8u39LRwsJCbNq0CW3atEHfvn05JQ8rKiqwf/9+Tn8bSt2YNGkSGIaBlZVVDd9v3bp1kJWVxdSpU7+56Un5b75ng/38/KCqqorNmzfXeGf6+flh0KBB1H4LwfdOmTx48AB9+/aFjIwM6xfy5Pvu3TuEhobS0xKUGtCAOUUoeIbozZs38PHxgbW1Nfbs2cNer6ioQNeuXTF06ND6mmKD4eugua+vL7S1tTk7oMXFxXBwcMDYsWPrY4oNkoKCAixduhSioqJs8JY/2GhhYcEpgUP5OZSUlGDdunUQERFh5e7q6gptbW1aj/8nwZNfXl4ehgwZAnNzc0RHR3PqUHbv3h0DBgygjvpPhpdp3qlTJzZo7u7uDj09PdZJpzL/eWRmZsLIyKjGpqebmxscHR2pLakD/HrKL8fJkydDTEwMu3btqnHibfLkyejevTvVcQHhl+/XmYgvX76Ep6cnOnfujB07dnCu5efns89SmQvG92xDdnY2Vq1aBR0dHYwfP55zraioCKdPn6YBXAHhl3VmZiZycnLYoGxBQQEiIyNhZmaGAQMGcIJZ/M9RmQsGvz3g/3vx4sVgGAbh4eE1ArhLly6Fs7MztSUCwq+nZWVlNd6Lffr0ga6uLrZv317jtAS134LDL+9r164hKSmJrVkO/Ju80qJFixpBcx40aE7hhwbMKULDM0gZGRnw8vKCg4MDGzT38PCgAYCfyLeC5vr6+mwTUFdXVxgYGFB5/2Ty8vIQFBQEhmHY7K3Kykq4urrC3NycOui/iNLSUqxbtw6NGjWCrKwsJ1hOnZifA89+5+bmon///rC2tkZ0dDTKy8vh6ekJXV1dak9+Effv38egQYNgb2+Ptm3bQlNTk+r3LyQzMxP6+vro2LEjXr58CQ8PD47MqR0XnK8DAF8HVoYNG4ZmzZph37597KlDHjx7Qu1K7eCXNa/HR+fOnbFlyxa8fv0aAPD8+XM2aL5r167//A7Kj+GX17FjxxAREYE9e/awJ2mzsrIQEhICfX19TJgw4ZvfQe1K7eCX9aJFi2BtbQ1NTU14e3vjypUrAIAvX74gMjIS5ubmGDRoEH1P1hF+mRcXFyM3N5dzfdasWRATE0NkZOR3+6pQ+107+GUdEhKCXr16QVtbGxEREZw62r1794aenh527txZ431K7Xft4dfLuXPnQlNTE23atEH79u0xc+ZM9lpqaioGDhwIJSUlJCQk1MdUKX8QNGBOqRP8QfMePXrAyckJGhoadDH6C/hW0NzY2Bi6urpU3r+QgoICBAYGQkREBNu3b0fv3r2pvH8DpaWlWL16Nbp160aDib8Inv3OycnBgAEDYGdnhzZt2tAArhB8vaD50WLy/v37cHV1RefOnamsfwNv376FkZERGIaBrq4ulXkd4Nf1FStWwMvLC1paWlixYgWnUfOwYcMgJSWFmJgY2lvlJ8BrfL1kyRKMHDkSpqam8PPzw4sXLwBUB8179OgBfX19Tn1timDw6+bMmTOhrq4OIyMjODo6wtTUlO0d9PHjR6xcuRJGRkYYPHhwfU23wTB37lwoKCggJiYGhw8fRpcuXdCyZUucP38eQHXQPCoqCmpqaggODq7fyf7B8NtvXsZ4mzZtMGvWLE4Ad9asWRAXF0d0dDS+fPnC+Q5qvwXH398fCgoKWLFiBRYsWAB1dXUMGzYM169fZ+/p27cvWrRogRMnTtTjTBsGixYtgqKiIi5evIicnBzMmDEDDMNwTgWlpqbC1dUVbm5u9ThTyp8ADZhT6gx/0Nze3h52dnZ0MfqL4HdSkpKS0L17d3Tq1InK+xdTUFDAZpq3b9+eyltAvg4m1jZboqioiNV5/tqVlP8mIiICKSkptbqXP9Pc09MTjo6OVL/rAH/Zjx/x8uVLVv5U1oIhzII9IyMDEydOpPr9k5gzZw6UlZWxePFiREREQFJSEqNGjUJycjJ7z/Dhw8EwDK0VX0d2796N9u3b4+bNmwCA06dPQ0REBLq6uhg4cCBevXoFAEhPT8fMmTPpRv5PYM2aNWjVqhWuXbsGAFi5ciUYhkGbNm3YwGJWVhaCgoIwePBgmgVaB86cOQMTExO2bMLx48chJSUFMzMzyMnJsSUo8/PzERcXR/X7JzB37lwoKytj9erVOHjwIGRkZDBgwABOuU9/f38wDIO4uLh6nOmfz8GDB6GhoYEbN24AqC4TwjAMNDQ00L9/f9y6dYu9d+7cuVS/60haWhrc3NzYjYf4+HjIyspixIgRaNKkCedU0IsXL6jtpvwQGjCnfBNBs+V493/69IkGAH4x/L/FgwcPqLx/ArVxTnJzc7F3715WzlTetYNfX6OiopCXlwfgx0FzmsEiHGfOnIGqqipGjx6NtLS0Wj3D+y0KCgqoPRGQmJgYHDx4EAAwdepU9O7du8YR5m/Br//UWRcMXnAQqJb/1zWda4Mwz1D+JS4uDu3bt2eDiTdv3gTDMJCTk0Pfvn1x+/Zt9t7FixdTe1JHDh48iHnz5gGolr2cnBwiIiKwatUqSEtLY8iQIUhPT+c8Q4MuwvPhwwf069eP7TNx7NgxSEpKYu7cuXBwcIC6ujor79zcXNZfobZcOFJSUjBr1iwAwIkTJ6CgoICIiAjcv38f7dq1g5KSUo1TE1S/hefYsWPQ1NRkS97cuHEDYmJiUFBQQPfu3Tn1njds2EDtdx05e/Ys25Pp6NGjkJWVxfbt27F//340atQIgwcPZkus8qD6LTyFhYUIDw9HXl4eLl26BBUVFURERACobqTKMAwGDhzIeYbabsp/QQPmlBrwG42YmBjcv3+/Vs99/UKlxr52CGOkvw4m0uBi7Xn48CE+ffoEoPqIc22DivxQ57F28Ov269ev0bx5c9jY2ODz5881rvPDr88JCQmc4Avlx2zatAlmZmYYNWoUHj58WKtnqP0WnNLSUkyePBkMw8Db2xvNmjXD3bt3f/gcv37X9iQApZrExETY29vj5MmTrOx5dYX/C7oY+nlUVVXhzJkzCA8PB1AdfJGVlcWePXtw/vx5MAyD4cOH49KlS5zn6HuzdnzLnyssLMT79++RlZUFCwsLrFixAkD1Jme7du2gpqaGoKCg7z5PEZwLFy7g+fPnuHv3Ltq0aYP169cDAFavXg2GYdCoUSM8ffqUvZ/KvXb8VzPVqqoqeHl5Yc6cOey4q6srWrduDRcXFwBUzj+DS5cusfp84sQJNG/eHLt378aDBw8gLi4OX19fnD59mvMMtd+141v6/enTJ3z8+BHZ2dmwtbVl7XdFRQU6dOgARUVFLFq06HdPtUHwvbUKLyli1qxZGDJkCJvIEhQUBHd3d7i4uFC/kFJrRAiFwgcAIiJSrRazZs0i06dPJ3v27CGFhYU/fE5MTIwQQsjNmzcJIYSIior+2sk2AKqqqlh579+/n4SFhZE5c+aQ169fk7Kyslp9x9OnT0lJScmvnGaDISUlhTg6OpK9e/eScePGkeXLl5OKioofPldVVcX+nZeXx+o65b/h6XZwcDCZNGkSUVNTI1evXiVOTk4kPz+fiIiIcGRLSLUtYRiGEELIhg0bSJ8+fUhlZeVvn/ufCE+Ww4cPJ2PGjCHJyckkNDSUpKWl/edz/Pb70qVLhBBqv2tDo0aNyJo1a4iWlhY5evQoWbhwITEyMvpPfeXX7/Xr1xMzMzPy9OnT3zXlPx5lZWXSuHFjMnr0aLJt2zby4MEDoqam9kOZ82zRvn37yIYNG37XdBsEX9tohmGIkZER6dmzJ8nNzSXLli0js2fPJv379ydWVlakXbt2ZMuWLeTChQuc5+h788dUVVWx9iEnJ4dkZWURQgiRkJAgSkpKJDMzk7x794506tSJEEJIZmYmsbCwIAsWLCCBgYGEEMI+T6kd5eXl7N/8dsTe3p6oq6uTpKQkoq+vT4YOHUoIIURVVZX079+fBAYGkrZt27L3U7nXDp4tTklJITdu3GDlLycnRz5+/Eju3r3LyjUvL49ISkqSqKgocvz4cUIIlbOgfG2/CSFEX1+f+Pj4kPz8fLJixQoyffp0MmDAAKKpqUk0NTVJTEwMSUxM5DxD7fePKS8vZ/WbZ6sJIUReXp4oKCiQ/Px88v79e6KpqUkIIeT9+/fEzs6OrF69mvj7+9fbvP9EPn/+TAj5d60SFxdHwsLCSFJSEsnJySGNGjUi5eXl5M6dOyQvL49ISEiQ4uJikpKSQvr27UtOnDjxzTUohfJN6jNaT/nf4etdttWrV0NeXh63bt2q0a35a/h3+zds2ACGYZCamvpL5tlQmTFjBlRVVdGrVy9YWVlBWVkZO3bs+OaOPr+8165dizZt2tQqw45STXBwMJo3b46mTZsiMTERwH9nH/LLe/PmzZg6dWqNBjiU77N69WpIS0vj4sWLSE1NRWxsLLS1tWFkZFQj05xf1pGRkZCVlUVMTEy9zPtPhT/bIjo6Gqampv+Zaf61zBmGoRn9P4DfXhQWFmLIkCHw9fWFqKgoDhw4AKBarl9nwvH/NpGRkZCTk8P+/ft/z6T/cKqqqli5BwUFoXHjxrC0tER8fDx7z7fsOP9vEBERAQkJCZw6derXT7iBwN+s88WLF/jw4QPHL8nIyIC+vj5b4zYrKwvjxo3DsWPH6CkVAdixYwenTFBAQABMTEygrq6OefPmsdlxDx48gLGxMWbOnInLly/D3d0dvXr1oiVBhODrEjbR0dEYP348Fi9ejGfPnrHjCxcuRPPmzfHu3TuUlpbC29sb/v7+7HWq5z8mODgYx44dYz9PmzYNrVu3RpMmTdClSxccPXqUlaOvry90dXWxbt06ODo6olOnTuw1qt+CwW+/Hz16hNevX3PWLx8+fIChoSH27NkDAPj8+TP+/vtvJCQkUL0WgOXLl3M+z507FxoaGtDU1ETfvn3Z8QcPHsDAwADTp0/HoUOH4Obmhm7durH2m8q8dkybNg3jxo3Dx48fAQDTp09HixYtoKGhAVVVVUyaNIltQL5nzx6Iioqia9euMDExgaGhIevD0NMqlNpCA+YUFBUVcT6XlZXB19cXixcvBoD/dFS+DrbIycmxAQNK7di3bx9UVFTYo/kXLlwAwzA4evRojXu/Je+9e/f+trn+qfAHW2JjYyEjIwNlZWWsXbsW7969+8/neERFRUFcXJw2vxGAyspKDBkyhNOVvLKyEsnJyVBXV4eVlRXy8/MBcGsKR0ZGQlpamq0NTflv/msRGRkZCRMTk28Gzb+2J82bN6cy/wH8sj569CiSk5NRVlaGyspKTJ06lRM05/H1BjLVb8Hgl3l5eTmuX7+OU6dOwc3NDV26dPmuz8H/HG8Djsq8dgQGBnLsQ0BAANq2bQs9PT14e3tzAritWrXC5MmTERMTA1dXV9jb29MAgABcuXIFDMNg5syZAKoTT1q1aoWwsDAsWLAAEhISGDRoELKzswEA8+bNg66uLlRUVGBjY8M2saWL/9ozbtw4ODg44Pr16wCABQsWoFmzZujTpw8kJCTQrVs31gdPS0uDra0tZGRkoK+vDx0dHRpwEYCPHz+iVatWcHZ2RmJiIo4ePQodHR2cOHECV65cgb29PWxtbdmg7Y0bN9C3b18YGBjAy8uL1W8aLK8906ZNQ25uLvt5zpw5UFVVRYcOHeDk5ITMzEwA1U3K1dXVMXToUGzZsgUuLi6wsrKi9lsA7t+/D4Zh4OPjA6B6Td+yZUvs2LEDa9euRdu2bdGpUyf29wgNDYWuri7at28Pe3t7ar+FYNKkSTAzM8OcOXNw7tw5dO/eHdevX0dFRQVCQ0NhZWUFPz8/PH/+HED1bzJo0CBMnz6dtd1UtymCQAPm/8/x8/ODr68vgH+NdVFREbS0tDBlyhT2Pv5rvGzmr7PlaABAOEJDQzFs2DAAwO7duyEtLY0NGzYAqK5Pyau3TeUtHF/X0S4tLUVhYSGCg4OhpqaG5cuX4/379zWe4zkxwL/yPnTo0G+Zc0PC29sbtra2Ncbnz58PhmFgaWnJ0e2IiAhISUlRWdcSfv3ev38/5s+fj9WrV7PN+IBq/TU1NeU0Av06mEjtyY/hX9DMmjULampq2LlzJxvI+vz5M6ZNmwZxcXHs2rULubm56NmzJ4YPH84+GxERQTcmBIBfT5cvXw4/Pz9W3mlpaejevTu6dOnCsRfr16/nZNZFRUVR/RaA27dvQ15eHk5OTgCqa9wqKSkhJiYGK1asgIWFBTQ1NdlMxW3btkFFRQW6urqws7OjAQAhiImJQePGjREYGIjQ0FDOJtDVq1chKSmJfv36sQkuvNratEmzcCQlJUFLSws+Pj44ceIE+vTpwzY6fP36NWxtbdG1a1ccP34cQPWmZ1hYGEJDQ2nARQB4NuDly5cwNTWFj48PAgICOBm5nz59gpubG6ytrTl6n5WVxT5P9bv2pKamQl1dHaampigsLMSlS5fQsmVL/PPPP9iwYQO6du0KZWVldi1/7NgxaGhowMjICJ07d6b2W0Cqqqpw/vx5KCsro0+fPti5cye2b9/OXn/48CG0tLRgYWHBJgi9ePECL1++pPZbQPh1MigoCNbW1vDz88OgQYNqVDuwtLTE8OHD2aA5/3Uqb4qg0ID5/2OqqqrY7Djg3wBhUVERhg4dih49euDVq1ecZ27fvo0ePXrg5cuX7Nj69etpAEAIeC/K0aNHo2/fvrh69SqkpKTYYDkAhIWFITAwkGPco6KiICMjQ+VdC/iDLQsWLICTkxPOnDnDjs2dOxdqampYtWoVPnz4AADo27cvHj16xN4TGRlJ5V0Lvpf9c+jQIejr62Pjxo2c8T179sDPzw/Gxsbw9vYGANy8eRNt2rShshaCmTNnQklJCT4+PuzCZ9OmTez1yMhImJubo2/fvhz7HR4eDjk5OSpzAViyZAmUlZVx+fJlzsYaj1mzZoFhGBgYGEBHR4e959y5c2AYhspaCGbMmAEVFRVERERwGu2lpqbCxcUFDg4OCAwMhIeHBxQUFNhgVnR0NJo2bUo34ASgrKwMJ06cgJ6eHpycnLBx40Zs3rwZQLXfeOvWLZiamqJ9+/Zs0Pzp06d4/fo1DQDUgf3796NRo0YQERFhbTdvkZ+UlAQpKSn079+fPYbOgwZuBYOno7du3UKHDh3YUxH8iRNPnz6Fra0tunTpghMnTtT4Dirz2sOT1YsXL2BsbMw2BeYnOzsb7u7usLW1xaZNmzj+JM0sF4zKykpcuHABVlZWMDU1xYYNG9gGn0B1ANfJyQmKioqsL/jmzRt8/PiRblDUgYSEBKioqIBhGKxbt45zLS0tDdra2rC2tmY3/HlQ/RYMftsbEBAAJSUlaGlpscmFPCIiImBjY4OePXvSsrWUOkMD5v9P+XrnODo6Gurq6uzuZ1xcHJo0aYJp06axGYlZWVnw8vJC165dWQN/5coViImJ0TrDteB7L8WrV6+ibdu2YBiGE+AqLCyEh4cHp5zFwYMHwTAMYmNjf/l8GxIzZ85EixYtEB8fX+PFOWfOHLRt2xZ9+/aFvb09FBUV2QDXli1bICkpSQNcP4Bft48fP44dO3awdbCzsrIwcOBAODk5ISwsDJWVlfjw4QM8PT0RFBSEzZs3o127dmzN0Pv379fL/8OfzLp169C2bVvcuHEDQLU9FxcXh6mpKWehtGrVKgwbNoz9vZKTk9G0aVNaR1sA8vLy4OjoyC6IXr9+jYSEBAwfPhzz5s1DXl4egOrSWjExMaxzX1FRgcLCQty8ebPe5v6ncuDAAbRs2ZItnwAAJSUlePPmDYDqQMzw4cNhb28Pd3d31n5nZGSgR48e9H0pADzfsKysDMePH4eJiQlERUURERHBuef27dswMzODlpYW6zfyoAGA2vGtDM4jR46gadOmGD58OHtKgnfftWvXwDAMgoODf+s8GyI8Hb158ya0tbUhKSlZIzD+7NkzODg4wNjYGBcvXqyPaf7RfEu/MzIyYGFhASMjoxq9JLKzs2FpaYkxY8b8rik2OPh7GSQmJsLOzg4iIiI1amynpaWhS5cuaNmyJV68eMG5Ru137fhav8vLy5GQkAANDQ04OzvXuO/Ro0eQlZXFqFGjfus8Gwrf08tFixZBQ0MDM2bMqFFiNSQkBKNGjaI6TakzNGD+/5SvsyMuXboEY2NjmJubs434du3aBRUVFZiamsLQ0BDm5uYwMjLi1JN7+/Yt7t69+9vn/6fBb6zj4uKwYsUKbNiwAVeuXAEATJgwAVpaWli4cCFycnJw7do1uLq6wtjYmLPTX1RUhHPnzv32+f/JJCYmQkNDA8nJyQCqa2V/+PABcXFxrCMTGhqKMWPGwM/Pj5V3cXExJk+ejCNHjtTb3P80Zs2ahWbNmkFTUxMMw2D+/PlsYGvEiBHQ0NBA8+bN0aFDB+jq6gKo/n3atm1bowEX5fvw25OSkhLMnj0bISEhAIDDhw9DVlYWQUFB8PDwQPv27TnZ/Tydr6qqwqdPn2iDZgHJzc2Fvb09Zs6ciZ07d6J3795wcHCAjY0NjI2NMXLkyBrZWTQbsW4sXboU3bt3BwDcu3cPK1euhLa2NmRlZbFo0SIA1eXL8vPza9RezcrKqp9J/4Hw2wag2rb8888/0NfXh7m5eY3779y5A1VVVfTr1++3zrMhwG/DCwoKONf2798PMTExzJw5s0Z5hAcPHtDsTyH5XtDkzp070NTUhJeXF6eUGQA8fvwYf//9Nw24CAi/vF6+fIkvX76wp1GeP38OY2NjODs711jPfP78mcpaSL623xUVFTh37hysra2hoaHBru15PHr0CIaGhvDw8Pjtc/3T+dp+83owVVVVISEhAQoKCuzJWd44ALx69Yr6g0LAL++LFy/i+vXrbN83oDrxzdTUFP7+/uxpcR60ITblZ0AD5v8POX/+PFuWws/PD5MnTwZQHbgyNzeHsbEx+2JNSkrCrl27MGvWLGzatIl11KnDLhwzZsyAmpoa3N3d4e3tDVlZWRw5cgRv376Fv78/VFVVISUlBUNDQ3Tt2pVdLFVUVFCZC8mxY8egqqqKoqIiPHz4EHPmzIGGhgakpKQ4QQB++dImQ7WDXz63bt2CtbU1kpKSUFxcjKioKEhKSmLmzJkoKipCUVERnj9/jvXr1+Pw4cOsvCdNmgQ7Ozvk5OTU1//GH8uOHTtw7949ZGRk4M2bN0hPT4empiZWr14NADh16hSkpaXRrl077Nu3j32O1qasHf+V0WJiYgIJCQnMnTuXzT4cOXIkzR6qI9/SzdjYWDAMgyFDhqBDhw7w9fVFeHg4QkJCwDAMp0QLUP27UR0XDH5dLy4uZoO4paWlOHHiBDp06ABHR0eOXKuqqpCenk4DAALCL8OQkBD07NkTPXr0wIULF1i5792797tBc4D64ILCL7uDBw9i3bp1OHXqFHuM//r16+jQoQN8fHxqBM15UH9QcHgNarW0tDgnlp8+fQojIyM4OzsjISGhxnNU1oLBL68vX76wDSYrKipw8eJFmJqactb2PF69ekVlXQcWLVoENzc3dOzYEfHx8WwzbF7QnNcI9GvoO7P28NvuadOmQUlJCQoKCrCwsEB4eDh7jRc0DwgIwNu3b7/7HRSKMNCA+f8jqqqqUFBQAH19fTg6OqJPnz6QlZXFnTt3AFS/cM+fP88Gzb8+ZsuDGnrhiImJQatWrZCUlAQA2LhxI0RFRbFt2zYA1dlcOTk5OHPmDB4/fkxrgQrBtxy/9PR0GBsbQ1tbG4qKihgxYgS2bNmCFy9eoFGjRrSckBB8fapk+fLl+Pvvv2scpY2OjoakpCRmz55dw4G5efMmJk+eDGlpaXpKpZZ83QBRRkYGqamprI3YvXs3TE1N2RqJ8fHx8PLywurVq+miSED45XXw4EGEh4dj0aJFrB6/e/cOT5484TzTtWtXdgOaIjj8Ms/NzUVpaSmbuRUVFQVnZ2ds3LiRPUL+4sULWFlZ0dMpP5GFCxeiW7duMDMzY9+NFRUVOHHiBHR0dNhGoF9D/cLawa/jK1euhLS0NGbPns32O1i3bh0b1Nq3bx+aNGmC0aNHU/nWAf5gyfTp06GoqAh1dXXo6Ojgr7/+wuvXrwFUB801NTXRu3dvWoJFSPj1OyYmhm0WPHnyZDg5OcHFxYUtu/fs2TOYmprCxMQEt27dqq8pNyiCg4Nha2sLXV1dTj+sixcvomPHjjA1Na0RNAeo/a4t/Pq9atUqyMvLIzAwEF5eXmjatClCQkLY5J+EhAQoKyvD3t6+vqb7x8Nvu+/evQsdHR0kJyfj9OnTmDFjBlRVVdnTtUB1TXNVVVVERUXVx3QpDRgaMP9/SGFhIVRUVCAqKoro6GjONV7dMwsLC055ForgfB2gWrp0KQYPHgyguhGilJQUa9Tz8/O/WbuZBrlqD7+skpOTceHCBXZzIiUlBQsXLsSRI0fYzIusrCxYWFjg/Pnz9TDbP5cBAwZw6uoD1TXiGYaBubl5jRIIGzduhKysLMaNG8e5tn37dvj4+HCO1VFqR1paGoKDg9nazDyncu/evdDU1MThw4fx5csXeHp6wt/fv0aZCkrtmTp1KhQUFGBnZwclJSV06NABERER7PHyvLw83LhxA66urtDX16cbnELCvzBasmQJunbtCgMDAwwaNIgNpvBqOldWVqK4uBiurq7o3LkzfU/WAX7ZhYSEQElJCXPnzsXAgQMhIiKCxYsXo6Kigg2a6+vrw8DAoB5n/GfydYbbw4cPMXz4cE527YgRI2BkZIS1a9eyvveWLVtgb29PM+SEhF9u9+7dg6enJ27fvo3CwkJs2LAB9vb28Pb2ZoPmN27cgLS0NPz9/etryg2CEydOYPr06diyZQs7FhMTA2dnZ3Tr1o1d7zx+/BhDhgyhNlxI+OUWGhoKZWVlLF68GOPHj4eoqCimTp2KoqIiANVBcysrK6ioqLCZ0BThePLkCaZMmcKe1geABQsWQEZGBitWrGDXmSdOnICbmxvV7zqyadMmDB06FNOnT2fHXr16hYCAAKioqGDlypXseFRUFF3rUH46NGD+/wSesa6oqMCbN29gZmYGHR0dODs712i8wguaq6qqYtiwYfUx3QZFfHw8MjMzsXjxYkyZMgVxcXGQlJRkm2hVVVUhJiYGwcHBdINCSPgXRbNnz4aenh7atm0LMzMzuLu7c+4tLS3Fu3fv4OnpCUtLS/piFZDnz5+zWZ8vX75kx1euXAmGYbBq1So2mMgjNDQUXbt2rbHo/94pFsr3SUxMBMMwkJCQqNGM9tGjR3BxcYGqqipat24NQ0PDbx7np9SOQ4cOoWXLlrh79y6r88OHD4e5uTl27NgBAPjnn3/g6OgIDw8PTgktinDMnTsX8vLyiIiIwOzZs+Hl5QUJCQkkJiYCqD5uvnPnTtjb28PExISWz/pJpKenIzg4GKdPn2bHIiIiwDAMFi5cyAbNDx8+jAEDBlAdFxD+ZuN79uyBmpoaOnToUKMJ8MiRI2FkZISwsDA26MKD2nDh2bt3L7p27YrevXuzNgOo3pCws7ODj48PGzRPTU2l+i0g/BniN27cgLGxMeTk5NgTtDwOHDiAbt26wcXFhW0Mz4PacOFJSUnBsmXLEB8fz44dPHgQoqKimDx5MrvZfObMGQwfPpzqt4A8fPiQ/fvYsWNgGAYtW7bkvC+B6hNasrKyCAkJYU968qD6XXvi4uLY6gcfPnzAwIEDIScnh4EDB3Luy8jIQEBAAFq3bo2goCDONarjlJ8JDZj/P4DfSJ85c4YNymZnZ8PMzAydO3fG6dOnazjjaWlp1OAIAc/IA9X1+1RUVPDs2TNs3boVEhISaNSoERssB6qDht26dcPUqVPrYbYNC94RuaSkJJSXl2PhwoVgGIbNIi8tLcXWrVvRuXNnWFhY0ACXgPAvNCMjI2Ftbc3JkAsODoaIiAjWrl1bI2jO35CILvxrz7ec7OXLl4NhGAQGBtbIaH78+DGOHz+OnTt3snpNs56FIzw8HGZmZigoKGBlWFVVhd69e8PExIS9Lzk5mZbQ+gm8fv0axsbGOHToEDvGaxgsLy+PtLQ05OTkYOPGjZg6dSrtqVIHUlNTWTt8/vx5MAwDOTk5HDt2jHNfZGQkJ9Oc3x7R92btiIiIgJqaGtuMrKysDD179kSjRo0QGhqKkpISzv2jR4+GsrIyLRdXB9avX8+e4KyqqmJ712hqatawF1u3boWjoyPs7e05DeOofteODRs2gGEYPH78mDOmpaUFBwcHvHnzhnP/wYMHYWJiwpYvo4FEwbl79y5rN65fvw6GYdC0adMaNuPQoUMQExPjZJrzoPpdOyIjI9G+fXtOgtCMGTPAMAxWrFhRI1t/8eLFYBgGu3fv/t1TbRBERUWBYRhOWayUlBSMGDECUlJS2LVrF+f+169fY+LEiejRowddX1J+GTRg3sD5OvNWR0cHYWFhbND89evXMDU1hbOzM+Lj41FWVgYbGxsEBASwz9GXau3ZsGEDdHV1kZeXh7dv32Ls2LE4ceIEe33KlClgGAZ79+7FnTt3kJKSgm7dusHExIQTkKHUjq9lNXjwYGzatAkAcOTIEUhLS7Nlh3jO4pkzZxAWFkaDLXUgNzcXT548gaamJry8vDhlbYKCgiAqKop169bVyCCnui08u3btwqVLl9jPwcHBEBUV5Rx5/hbUfgsObwG/fPlydOjQgR3n2ZAnT56gadOmuHz58jefowjH48eP0bhxY847E6jOfraysmJrsvIaIwJUv4Vhw4YNMDAwYGvBA9Ul4xiGQUhISA07HR0dDYZhamSLUn5MVFQUREVF2fJZ/BtrHh4eMDQ0RExMDHuChceyZcuobgsJT1/5T2CVl5dj5cqVaN++PUaNGoW8vDzOM+Hh4Rg7diy14QISGRmJxo0bczY5eWzYsAHW1tYYMmQIMjMzOdfOnz9PZS0kGzZsgKamJqeHSnR0NMTExODv71/Dbhw+fBgMw2Dt2rW/e6p/PLzg7eHDhwFw1zBjx45FkyZNsGvXLjaDn8e2bdvo2lIIIiMjIS4ujri4uBrXUlJSMHLkSOjo6GDv3r2cax8+fOAkZVEoPxsaMP9/QmBgIOTl5XH58mU285NnVDIyMtCpUyfo6elBU1MTBgYGNZx3yo/hHV+OjY1lj2ypq6vj2rVrnPvGjBkDNTU1SEpKwsLCAp07d6aZzkLA72ynpKSgrKwMFhYW2Lp1K06ePAlJSUk2wFJRUYEVK1bUeAlTedeO2NhY1mGcOnUqRowYAaD6mKKuri7c3d05QfP58+eDYRgcOHCgHmbb8Pjy5QtatGgBW1tbjj2ZN28eREVFsXXr1vqbXAPgewv3N2/eQEZGBqNHj+aM37hxA1paWkhLS/sd02uQ8C9qeH8XFxfD0dERU6ZMqVGezMrKijZU/QnwAgDfCnAFBARATEyMLTfET1xcHA0ACEhkZCQnWM6Dl4lbXl4ONzc3GBsbfzNoDlAfRVB4Mv9WwKWsrAxLliyBtbU1xo4dW8PG8OwQDeTWjq83g3icO3eO/Ts8PBw2NjbfDJoDVNaCEhUVBRERkW/a79DQUDAMgzVr1rBjPJ1OTEyk9ltAIiMjISYmVkPW/KfI//77bzRt2vSbQXOAJmQJwsaNG78ZLF+yZAn7d0pKCkaNGgUdHR3s27evxnfQYDnlV0ED5g2QXbt2sV2ageqawx07dsTJkycBAO/evcP169cxdepU9vjWu3fvsH37dkRERNDMWyHYv38/GIZhMw7z8/MxYMAAMAzzTaOekpKCy5cv48GDB/QovxDwvxRnzpwJZ2dnPH/+HBMmTICTkxNkZGQ4ZW/evXsHd3d3hIeH18d0/2i+fPmCUaNGQVxcHD4+PpCQkOA4jN8Lmm/evJnqtJB8y+l78+YNdHR04OjoyDazBaqD5o0bN6a6LST8C/a4uDisXLkS27dvZ2W8Z88eSElJsc0nb968CQ8PD9ja2tLFvpDwy62kpIQTuJo/fz4MDAwQFRXFLkALCwvRqVMnrFix4rfPtSHBCwB8HeDir886d+7c7wbNAeqn1JZ9+/aBYRhcuHCBM96/f39MnDiRPcZfXl4Od3d3mJmZYfv27VS+dSA6OhpNmjSpod9DhgxhfZPy8nI2aD5hwoQameY04FI7eFnL/MFxAPD29oa5uTnHpoeHh8Pe3h6enp41msJTak9UVNQ3A7h3795lN9bWrl0LhmGwevXqb34HtS+148CBA2AYpkaPt549e2LChAmcU25jx46FpKQkoqOjabKhkFy4cAEMw7BltHj07t0bioqKeP/+PTuWkpKCMWPGoHnz5pymqxTKr4QGzBsYUVFR6N69O2dB+unTJ7Rt2xbLly9HcnIyBg0aBCMjI1hYWIBhmBr1oACa1SIIGzduBMMwEBER4TSx+fLlC3r06AElJSXcuHEDwPedcRp4EY7U1FR06tSJ3ahISkpiM/d5WVxv376Fm5sbrK2tqV4LyadPn6ClpQWGYRAaGgqg2vHmOd8PHz6Evr4+PD092Y05HtRBFx7eYp5nNzIzM6GpqQk7OztOpvnkyZNhZ2dHF/t1YPr06VBWVoaFhQX09PQgLy/PBg3j4+Ohrq6Oli1bon379rCzs6PNJoWEX15Lly6Fs7Mz2rdvDz8/P/b9OW7cOOjp6aFr166YPn06bG1toaenR21JHeBt6h85coQz7uHhgfHjx3PqaAcEBKBJkyaIjIz83dNsEGRnZ8PHxwdKSkocO92rVy9oaWnh1atXAMBJTunYsSOGDh1aL/NtCKSnp4NhGIwaNYoz3rt3b2hpaXGarpaXl2PZsmXQ0NDAqlWrfvdU/3gqKioQGRnJ8QeB6mCigYEBW+uZ399esWIFRo8eTd+XQhITE1OjzBAAuLm5wc/Pj5PdHBYWBnFxccyfP/93T7NBkJ+fj169ekFDQ4NzSpZnv3n6ze+P9O3bF507d/7tc20o8BKCOnXqxAbHe/XqBUNDQ1beX58qp2XLKL8TGjBvgPAMyNWrV/H27VsAwJw5c6Curo5GjRph8uTJbGMnLy8vTJw4sd7m+qfDqxsXFhaGUaNGQU5ODomJiez1wsJCuLm5oVWrVkhOTq7HmTY8lixZgh49eqBXr16cpitnz56FoqIizMzMoKmpCWtra5ibm9OyNwLC75x8/PgRAwcORK9evSAjI8NmuFRWVrIZFY8ePYKioiJtXvuTWLlyJRwcHPDs2TPO+Nu3b6GmpgZ7e3tcvXqVHaf1+4QnNjYWLVq0wNWrV1FVVYXnz58jKCgIIiIi2LNnD4Dq+uW3b9/G/fv36amgn0BAQADk5eWxdOlShIaGQkdHB7a2tmz98m3btmH48OFwc3PD+PHjqf2uA8XFxXB3d4empiYn4NK7d2/o6OhwapnzGDduHOzt7ak9EZJLly5hwIABMDIywvXr1zFo0CDo6+uzsubJlb8xMw0mCk9ubi4CAgLQqFEjtr5tz549oa+vzwZc+HW5vLyc0xibIhjFxcVYv349REREEBoaioEDB0JfXx/Pnz8HwC1vw/MRackb4SgvL0f//v3Rrl07Th+JXr16QVdXl5U5PwsXLoStrS2130KSkpKCv/76C7a2tjhw4AB8fX1hYGBQQ7/57QfV67rx9u1b6OnpwdLSEm5ubjA0NGQ3l/nXN/wnbAHqE1J+DzRg3oDgGY2qqiokJCRAQkICS5cuRUFBAYqKipCWlsYppVBZWQkbGxt6zFlIeEe2jh49CgBIS0vD4MGDvxk0d3d3h5qaGq5cuVJf021wbNu2DQzDQFlZGY8ePQLw70v1wYMH2LdvHxYvXozY2FjOopTyY/gdv5MnTyI1NRUlJSX48OEDxo4dC2lp6RrHQouKivD+/XvqvPwkUlJS0LRpU/j4+LBBc97vcuDAAYiIiMDCwgL3799nn6GLox/DLyOePHmbE/zk5uZi6tSpMDEx4WQnfv0sRXCePXsGbW1t9t0J/HsSyNbWFu/evWPH+W02td/C8+rVK/j4+MDJyQkHDx5ks7e+DgDw/0034QSHX1ZXr15Fv3790Lx5c6ioqLAb+/zvyG7dunFqtlK7IjyFhYWYN28eGIaBvr4+zMzMvqnfXzfkoz6LcJSWliIsLAwSEhKQlJRkT8Txy9PR0RFBQUHsZ2pLhCMnJweDBw+GjY0Ntm7dCh8fn+/ab162ObXfdeP+/fsYOHAgVFVVoaioiE+fPgHg+iFeXl7sKYuqqipqv+tIZmYmOnXqxClxy4+9vT3c3d2pTlN+OzRg3kD4lvGYOXMmW4qFv/5TQUEBbt++DTc3NxgZGdFFqJB8+fIFCQkJnLFHjx59N2huaWkJLy+v3z3NBsH3nJDY2FgwDIPx48f/sDYiXRTVDn5b4u/vDzU1NezevZttFvz8+XOMHTsWsrKybA+EHj16cDLLqawF42v95snv/v37kJaWhpeXFyfTPCYmBn5+fvD19aWyFpBvyWvz5s1o2bIlm83CIz4+HnJycrS550/m1atXaN26NZtNzsse//DhA5o3b/7N+qt0gSQ8PPvy6tUreHp6QkVFBSoqKuxGEL8P6OvryylTQeUuOF8HzX19faGtrY2LFy+y45WVlfDw8EDr1q1Z/afUnYKCAixduhSioqIICQkBwLX5Xbp0Qdu2bWlg6ydRWFiIjRs3QkxMjJU38K9+q6urU/2uIzz9zcnJQf/+/aGiooKWLVvi6dOnALj2u0+fPggODmY/U/tdN1JTUzFgwAB07NgRu3fvZscrKirg5uYGTU1Nqt8/mczMTBgZGcHc3JxzesLNzQ1aWlpU3pR6gQbMGwD8L8SDBw9ymkzOnj0bampqWL58OT58+AAA2Lt3L3r06AEnJyd6zFkIqqqqasiL//Pjx4/x119/QU5OjtMEsaSkhDrpQsAvs9TUVCQlJeHNmzdszdUdO3aAYRjMnDmTzQAAqKNYVxYsWAAlJSVcvnyZU/IGqC7RMmHCBDAMAwMDA3To0IE6MULCr9/79u3DwoULMWfOHFy/fh1Atc5LS0vD29sbp06dwrt37+Dl5YX169ezz1H7XTuOHDmCwYMHo2/fvpyg4PXr12FsbIygoCBkZmay46mpqdDV1eWczKIIxsuXL3Hnzh1OE7iPHz9CRUWFzTysrKxkF/3dunXD7Nmz62OqDRqenXnz5g18fHxgbW3NlhsCqm2Iq6sr2rdvT235T+BbQXN9fX22Cairqysn2EITV34eeXl5CAoKAsMw2Lp1K4Dq38PV1RU6OjqszKmP+HMoKSnBunXrICIiwgbNqX7/XHj2Oy8vD0OGDIG5uTmio6M5a3hXV1doaGhQ+/2T4WWad+rUiQ2ae3h4cIK3VL9/LpmZmdDX10fHjh3x8uVLeHh4UHtCqVdowPwPhz/YcvfuXejo6MDZ2ZnTeM/f3x+tW7fG8uXLkZ+fj+zsbCQkJNAyFULAL+/Pnz9zPvM7KY8fP8aQIUOgoKBQowkiDZrXHv4FzaxZs6CpqYlmzZrByMgIffr0YYMwvKD57Nmz8fHjx/qaboMhOzsbdnZ2iI6OBlDtvFy+fBl///03wsPDkZOTA6C6XvzGjRupLfkJTJ8+HW3atIG3tzcGDhzIaciclpYGPT09qKqqQkVFBaampnRRJCBRUVGQlpbG6NGj0bNnT7Rr146TMRQcHAxtbW1MnDgR586dw/3799GtWzfY2NhQmy0k27dvh66uLpSUlKChoYFz586x1zZt2gQxMTFs2rSJHSsvL4exsTEtE/eL4OlxRkYGvLy84ODgwAbNPT09OQEAuglXd74VNDc2Noauri5d/P9iCgoKEBgYCBEREWzfvh29e/emMv+FlJaWYt26dWjUqBFkZWWhra1NZf2T4dnv3Nxc9O/fH9bW1oiOjkZ5eTm8vLxoAPcXcv/+fQwaNAj29vZo27YttSW/gbdv38LIyAgMw0BXV5fKm1Kv0IB5A8Hf3x/Dhg2Dvr4+GjduDDs7O0590Dlz5qBt27YICAhAfn4+O04DAcKxZMkSWFpawsPDAytXrmTHvw6ae3p6wtXVtT6m2CDg6eeaNWsgJyeH06dPIyUlBeHh4bCysoK9vT2rz3v27AHDMJzMW4rgVFVV4f3799DV1cXy5ctx6NAh9O/fHzY2NjAyMoKxsTECAwP/85QFpXbwHL/Y2Fi0atUKN27cAFBdCoRhGE5A9+3btzhz5gzi4uLoBoWA8IKzsbGxAKpLf9jY2GD//v1sqSEACA0NhZOTExiGgaGhIaytrVmbTt+VghEZGYnGjRsjMjIS9+/fh4ODAywsLNjrBQUFCAgIAMMw6N+/P8aPHw8nJyfo6upSvf6F8AfNeScNNTQ0aADgF8EfNE9KSkL37t3RqVMnKuvfQEFBAZtpzn9ygsr811BaWorVq1ejW7duVNa/CJ79zsnJwYABA2BnZ4c2bdpQ+y0EX/t0Pzpxcv/+fbi6uqJz585U1kIgzImejIwMTJw4kcqbUu/QgHkDYP369ZCWlmZLVVy6dAmmpqZwcXHBsWPH2PvGjx8PHx8fegxRCPhfrOvXr4ecnByWLFmCvn37Ql9fHyNHjmSv8wfNMzIyaKBFCG7evMn+XVJSUqMuX3l5OY4fPw4zMzPMnTuXlfGZM2foC1VAvqefc+fOhYqKCpo1a4bZs2ez9fp79+6NcePG/c4pNjhOnz7NscPr1q3DsGHDAFQ39JSUlERUVBSA6iO4L1++rPEddIOidsTFxYFhGISFhXHGTUxMYG5ujjZt2sDZ2Rm3bt0CUC3vO3fu4MGDB+y/DWpTBGPz5s1o1KgRx/84ffo0fHx88M8//+D8+fNsz4kjR47Azc0NPXv2xJgxY1hZU/3+dfAHze3t7WFnZ0cXpL8QfltP7crPozY2Ijc3F3v37mVlTWVeO772C2u7jikqKmL1nZ6Cqz0RERFISUmp1b38meaenp5wdHSk9rsO8NfJ/hEvX76k9lsI+PsDxcTEoLS0VODvEOYZCuVnwQAAofzRjBgxguTm5pJDhw6xY0lJSWTgwIGkVatWZM6cOcTNzY0QQkhlZSURFRUlAAjDMPU15T+W8+fPk+TkZKKrq0s8PDxIfn4+2bt3L1m5ciVxcHAgmzZtIoQQUlZWRho1asQ+V1VVRUREROpr2n8UUVFR5O+//yZpaWlES0uLEEJIt27diJycHNm3bx/n3pEjR5KXL1+SU6dOceRbUVFBxMTEfuu8/0T49fLIkSMkOzub5OfnEz8/PyItLU3S09MJAPZ3IKT6tzAzMyNLly6tr2n/0eTk5BAzMzPSpEkT8vDhQ8IwDAkODiZ3794lgwYNIn5+fmT58uXk77//JoQQsnPnTnL16lWyYsUKIiUlVc+z//M4cuQIGTJkCPHz8yMTJ04kbdu2Jb169SJ37twh/v7+RFRUlCxdupQ0b96cXLlyhYiLi3Oep7ZbMF6+fElsbGxI27ZtyZUrV9hxJycnkpaWRsTExEhlZSVRVVUl+/fvJ+rq6qS8vJwjd2q/BedrPf2Rj8e7Pzs7mzRv3pyIiIhQudcSYWzC178H9cEFIy0tjSgqKhJ5eXni7+9PhgwZQrS1tQX6DqrftYNfN6Ojo0m/fv2IjIzMD/We6rRwnD17lgwbNoy4u7uTyZMn10qveb9FYWEhadq0KbXfAnDgwAEiIiJCevXqRaZNm0YyMjLI9u3biYSExH8+x6//1C+sPRcuXCCBgYFkzpw55OTJk2Tt2rXk1atXRE1N7T+fozKm/E9Rb6F6Sp3h7XKOGzcOLi4uALgNKTdv3gwJCQl4e3vjzJkz7HM0w1w4Ll26BFVVVbRo0QKXLl1ixz9//oyoqChoampyMs0pghMZGQkxMTEcPnyYHauqqkJQUBAsLS2RnJzMyXQJDw+Hra0tp6wCRXCmTp0KJSUlGBsbo2XLlmjdujViY2NRXFwMoDrrNjk5Ge7u7tDX16eZFXWgqqoKV65cgb6+PoyNjVFVVYUbN25AR0cHTZo0werVq9l7v3z5Ag8PD4wfP57a7Tpw4MABqKqqYtKkSejWrRsMDQ05WfvHjh0DwzCcGtsU4cjNzUV0dDRUVFTYkyh9+/aFtrY27ty5g/z8fGzZsgWtWrVCUFAQp+EnQP0TYeB/J8bExOD+/fu1eu5rO06z+n/M102a165dC39/f2RkZPxnBhy/Xj958gRFRUW/dJ4NiXv37kFRURHr1q3D2LFjwTBMrXSc/7fKzc39hTNsOPDL7PXr12jevDlsbGzYfkHfyzTn1++EhATcvn371060gbFp0yaYmZlh1KhRePjwYa2eofZbcEpLSzF58mQwDANvb280a9YMd+/e/eFz/Ppd25MAlGoePXoEZ2dntGnTBrKyskhNTQXw3/rKL++9e/fSUquUeocGzP8gvueoHDhwAAzDYP/+/ZzxnTt3wt3dHebm5uxxf4rwZGRkIDAwEHJycpg4cSLn2ufPnxEdHQ1paWksXbq0nmb4Z7NlyxaIiopyjvEDwMePH5GVlYUOHTrAxcUF58+fR3FxMT5//gwnJycMHDiwnmbcMNi7dy8UFBRw7949tpFtr1690K5dO5w9exZAdU3tjh07wsXFhTaF+wlUVlbi6tWr0NLSgqWlJQAgICAACgoKWLx4Me7fv4+rV6/CxcUFxsbG7MKIBhMFg19e+/fvh5KSEmRkZFi95nH27Floa2vjwYMHv3uKDQqeTfj8+TO2bt0KRUVFKCkpwcTEBO/fv2fvq6yshIGBAS3t9BPg1/GZM2eidevW8Pf3R0FBQa2fS05O/mXza6jMmDEDqqqq6NWrF6ysrKCsrIwdO3Z8czOZX9Zr165FmzZtkJGR8Tun+8cTHByM5s2bo2nTpkhMTATw32VC+GW+efNmTJ06lSZWCEBQUBB69uwJQ0NDMAwDMzOz7wbN+WW9fv16yMvLU5tSS/hluXHjRpiYmNQqaM4v84sXL/6y+TVUtLW1ISIiwiao1DZ4Gx4eDnFxcTx58uSXz/FPp6qqitXvoKAgNG7cGJaWloiPj2fv+ZYN55d3REQEJCQkcOrUqV8/YQrlP6AB8z8EfqNy9uxZHDhwAEePHkVJSQmA6oVSo0aNsHXrVjx9+hTZ2dnw9PREdHQ0jhw5AoZhaDBAAL7niL9//x7BwcHQ0NBAYGAg51peXh6OHDlCA4lCcPnyZTAMgxkzZnDGe/fujVmzZgGoroFmZGQEQ0NDqKmpoWPHjjAwMGADuDSYKBwhISFwcHBAWVkZZ7HfvXt3mJqasp+TkpJo7T4huX79Oo4fPw4AnFqq169fh7q6Ouzs7AAA8+bNg4mJCRiGgaWlJZydnekGhYD8VxAlLi4OKioqGD9+PJvlAgCenp5wcXGh/SaEJDExEStXrsTo0aNRWFgIoPp0xNatW6Guro5+/fqx91ZWVuLz58+wsbFBSEhIfU35j+drXV29ejXk5eVx69YtgYLlGzZsAMMwnH8PlP9m3759UFFRYTMNL1y4AIZhcPTo0Rr38ss6MjIScnJy2Lt372+b658Mf8AlNjYWMjIyUFZWxtq1a/Hu3bv/fI5HVFQUxMXFERcX98vn21BYvXo1pKWlcfHiRaSmpiI2Nhba2towMjKqETT/Wr9lZWURExNTL/P+U+H37aKjo2FqavqfQfOvZc4wDM3o/wH878vCwkIMGTIEvr6+EBUVxYEDBwBUy/XrdST/b8Oz318nJ1Jqwi9v3lrn1KlTcHNzQ5cuXViZ/9dzPHty8ODBXz5fCuVH0ID5H8b06dPRunVrtG7dGm3btkXbtm3ZhU5QUBAkJCTY67q6uigpKcGdO3fQvn37bzaOo9SE32Dv2LEDQUFBmDBhAm7cuAGgujt5cHAwtLS0agTNedDglmAUFhbC1tYWtra2bGCxX79+6NChA0dvP336hOPHjyMkJATbtm2jjZzqAM8xnDNnDjp06MCO846K37x5Ey1atKhxXJEGFQUjISEBDMOAYRhYWVlh6NChOHz4MNsE5/r16zA2NoaNjQ2A6kZZiYmJePXqFd2gEBB+3YyNjUVkZCQiIiKQk5PD6jsv2DV+/HikpaXB09MTHTp0YDcmqH4LxrZt26CtrQ1/f39s2rSJcy0nJwdbt26FkpISRowYwY67urrCxMSE6rWQfF3Oo6ysDL6+vli8eDGAf/2PH2Vv8QIA31u8Ur5NaGgoe2pz9+7dkJaWxoYNGwAABQUF+PTpE4CawRZpaWm6+K8lX5cGKS0tRWFhIYKDg6Gmpobly5dzTq3w4G80yZP5oUOHfsucGwKVlZUYMmQIxo8fzxlLTk6Guro6rKyskJ+fD4DbhI/qt2D8l58RGRn53Uzzr+138+bNqcx/AL+sjx49iuTkZJSVlaGyshJTp07lBM15fL2BTPW79vDLe/ny5fDz80N2djYAIC0tDd27d0eXLl04dnn9+vVsCVCgeqOTypvyvwQNmP9BbNmyBXJycrhx4wbevn2L1NRUuLq6QkVFhT3eee3aNfzzzz+IjY1lnfVp06bB2NiYdeIptWPatGlQVFSEs7MzLCws0LhxYyxbtgyFhYXIysrC/Pnzoaenh8mTJ9f3VP9oeEGTgoICODk5wcrKCtbW1tDT0+NkEX3PwaSbE7Xje/J78uQJFBQUMGnSJM74pUuXoKmpiadPn/6G2TVcnj59Cmtra5ibm8PFxQUTJ06ErKwsNDQ04OXlhdDQUGzbtg0qKiro2rVrjQwXGsCtHfxymzVrFpSUlODk5AQ5OTm4uLjg9OnTnKB5mzZtICkpCT09PTbIQgO4grFz5040bdoUe/bsYRdEQHV5obS0NADVJ6+2bt0KZWVljB49Gj4+PtDU1KQnJ4TEz88Pvr6+AP7V+aKiImhpaWHKlCnsffzXeP4hDeDWDZ4tHj16NPr27YurV69CSkqKDZYDQFhYGAIDAzm2JCoqCjIyMlTWtYT/nbdgwQI4OTlx+jDNnTsXampqWLVqFT58+ACguk/Co0eP2HsiIyOpzIXE29sbtra2Ncbnz5/Pnn7jtyURERGQkpKiGxO1hF+/9+/fj/nz52P16tW4du0aOx4ZGQlTU1OMHj2afZd+nX1L7feP+dovVFNTw86dO1l/5fPnz5g2bRrExcWxa9cu5ObmomfPnhg+fDj7bEREBN2YEIIZM2ZARUUFERERnHVkamoqXFxc4ODggMDAQHh4eEBBQYG1KdHR0WjatCm1J5T/KWjA/A/C398fAwYM4Izl5eXBzs4Otra2NRb7Dx8+xNChQyEnJ1erphaUfzl+/DiUlJRw584d1klZunQpmjdvzjafeP36NaZNm4YBAwbQciBC8C2ZFRQUwNXVFeLi4oiKivrPeym1h9/RPnHiBCIiInDkyBH2SHl4eDg0NDQwcuRIZGRkICUlBZ6ennB0dKQB259Aeno6fHx84O7ujrt37yI7Oxtnz55Fjx49YG9vjyZNmkBNTQ0Mw9TYuKD8GH4dXbNmDVRVVXHz5k0A1TX6GYaBo6MjTpw4wdqSvXv3ws3NjZ5SEZJHjx5BX18fa9as4Yz36tULDMNASUmJEzTftm0bmjRpAi0tLbpBISRVVVVsdhzwbzZtUVERhg4dih49erAnV3jcvn0bPXr04JzUWr9+PQ0A1ILvvfuuXr2Ktm3bgmEYzqmKwsJCtkkzj4MHD4JhGMTGxv7y+TY0Zs6ciRYtWiA+Pr5Gzfc5c+agbdu26Nu3L+zt7aGoqMj+e9iyZQskJSWpfv+A7+n3oUOHoK+vj40bN3LG9+zZAz8/PxgbG8Pb2xtA9UnENm3aUFkLwcyZM6GkpAQfHx8YGRmhc+fOHHsSGRkJc3Nz9O3bl2O/w8PDIScnR2UuAEuWLIGysjIuX77MOYXCY9asWWAYBgYGBtDR0WHvOXfuHBiGobIWkAMHDqBly5a4fv06O1ZSUoI3b94AAF68eIHhw4fD3t4e7u7urLwzMjLQo0cP+r6k/M9BA+Z/EGPGjIGuri77mbcbt337dmhpaXGycYuKipCYmIhBgwbRjs4/YN68eTWOX+3btw/6+vrIzc3lLOrnzZsHWVlZVtbZ2dlsAIYGdWsPv6OemZmJwsJCdqygoABdunSBpaUlDh8+TBse/kSmT58OJSUlGBsbs2WbePVUt23bhtatW0NWVhbt27dHp06daJmKn8jjx4/RvXt3ODs74+rVq+x4RUUFjh49irVr18LX1/ebzjzl28yaNYvdDK6srEROTg6mTJnCLvQPHjwIWVlZLF26FNra2jAzM8Px48dr6DMN3NYenh2Oj4+HpqYmGxQHgGXLlkFHRwdXr16Fp6cnlJSU2CPlOTk5OHbsGOu3UJkLxtfvv+joaKirq7PlEeLi4tCkSRNMmzaN/U2ysrLg5eWFrl27sjp/5coViImJ0TrDP4DfRsTFxWHFihXYsGEDrly5AgCYMGECtLS0sHDhQuTk5ODatWtwdXXlNGkGqn3xc+fO/fb5/+kkJiZCQ0ODbR5ZWlqKDx8+IC4ujv23EBoaijFjxsDPz4+VeXFxMSZPnowjR47U29z/BPj1+/jx49ixYwdbBzsrKwsDBw6Ek5MTwsLCUFlZiQ8fPsDT0xNBQUHYvHkz2rVrh2fPngEA7t+/Xy//D38y69atQ9u2bdlSn9HR0RAXF4epqSmblAUAq1atwrBhw9jfKzk5GU2bNqV1tAUgLy8Pjo6OWLduHYDqZLeEhAQMHz4c8+bNQ15eHoDqXhQxMTGsj1JRUYHCwkI2+YJSe5YuXYru3bsDAO7du4eVK1dCW1sbsrKyWLRoEYDqtX5+fj5rz3lyz8rKqp9JUyj/AQ2Y/w/yvdIpZ86cgZ6eHtasWcNxyE+cOAEdHZ0aGRgVFRWcmlCUmty/f5+TZchj+/btkJSURG5uLgCwcszMzISysjJOnz7NuZ8Gc4UjICAApqam0NDQQHh4ONLT0wFUN4zr3LkzLCwsEBcXR4MrP4H9+/dDQUEBly9fRmVlJe7du4dp06ahVatWbPZEWVkZLly4wDlZQWX/80hPT0f37t3RvXt3XLhw4bv30aD5j7lz5w4sLCxgbW3NbniWlpbi0qVLyMrKwoMHD9ChQweEhoYCqK5dKS4uDjMzMzboRREcXoB2xYoVaN26NcfHuHHjBuu/ZGVloXPnzmjevDlycnI430HLsAjO1zK7dOkSjI2NYW5uzjbi27VrF1RUVGBqagpDQ0OYm5vDyMiIs/H59u1beuJQAGbMmAE1NTW4u7vD29sbsrKyOHLkCN6+fQt/f3+oqqpCSkoKhoaG6Nq1K6fUEH13Cs+xY8egqqqKoqIiPHz4EHPmzIGGhgakpKRgbm7O3scvY7rBLzizZs1Cs2bNoKmpCYZhMH/+fDYTdMSIEdDQ0EDz5s3RoUMHNmErMTERbdu2Zf11yo/h18mSkhLMnj2bbXp9+PBhyMrKIigoCB4eHmjfvj0nu58/KevTp0+0QbOA5Obmwt7eHjNnzsTOnTvRu3dvODg4wMbGBsbGxhg5cmQNW019lNrzrfhHbGwsGIbBkCFD0KFDB/j6+iI8PBwhISFgGKZGqc/KykoaR6H8T0MD5v9jXLx4EY6OjpxgCs+I5OXlYeTIkXB0dMT8+fORl5eH58+fw9XVFa6urtTYCMjX8jp06BCb+VlcXIyOHTuia9euKCgoYO95+vQpNDQ0cPny5d8614bInj17oKqqil27dmH48OHQ1dXFuHHj8ODBAwDVQfOuXbuibdu2uHjxYj3P9s+CX7d5jnpwcDCcnZ059z1//hwjRoyAi4tLjaAWQJ3GX0F6ejpcXFzg4uJC7UgdOXXqFFxdXWFhYcFmufEakW3evBm2trb4+PEjgOrmfP3798eIESOoXgtJbGws5s6dC6B6U5lhmP8Mvq5duxYuLi6cdyhFcM6fP8/WcPbz82P7piQmJsLc3BzGxsZs0DwpKQm7du3CrFmzsGnTJlpyqA7ExMSgVatWSEpKAgBs3LgRoqKi2LZtG4DqwFdOTg7OnDmDx48f001mIflWgDs9PR3GxsbQ1taGoqIiRowYgS1btuDFixdo1KgRPSEhJPyyvnXrFqytrZGUlITi4mJERUVBUlISM2fORFFREYqKivD8+XOsX7+ec9pz0qRJsLOz+6bPSPlvduzYgXv37iEjIwNv3rxBeno6NDU1sXr1agDVPo20tDTatWuHffv2sc/R9X3t+N5m2aJFi2BiYgIJCQnMnTuXXVOOHDkSo0aN+p1TbFDwyzs3NxelpaWsDx4VFQVnZ2ds3LgRL168AFBdisXKyoputlH+OGjA/H+MR48ewcHBAe7u7pxgCm+B/+HDB0yYMAF6enoQFxeHvr4+TE1NaWaFEPBkVVFRgYyMDMjIyKBXr17s8av4+HhYWFjA0tISFy9exMmTJ+Hh4VGj4Q2ldnytm7t27eLUv92wYQNMTU3x999/sxkU+fn5mDBhApW3gHzLDqxduxZ6enp4+/YtZ3zfvn1o1qwZnj9//rum9/+e9PR0uLu7w9zcHPfu3avv6fxx8Gfg79+/H87OzujUqRMeP34MoHpxuWTJEhgYGCAlJQWfP3+Gp6cnx95QmyI4fn5+6NSpEwDg1atX0NXVhaWlJVtflbdQAqqDie7u7pg4cWK9zLUhUFVVhYKCAujr68PR0RF9+vSBrKws7ty5A6Dazp8/f54NmvOy/7+G6nrt+Pq9uXTpUgwePBhAdUKFlJQU21slPz//m6UoqA8uGPzySk5OxoULF9gNipSUFCxcuBBHjhxhT3tmZWXBwsIC58+fr4fZ/rl8vbG5fPly/P333xgzZgxnPDo6GpKSkpg9e3YNX/HmzZuYPHkypKWl6SmVWsKv38uXL4eMjAxSU1PZzYfdu3fD1NSUbUIZHx8PLy8vrF69mtoSAeGX18GDBxEeHo5Fixaxevzu3Ts8efKE80zXrl3ZDWiKYPBv4ixZsgRdu3aFgYEBBg0ahFu3bgH493R+ZWUliouL4erqis6dO1Pdpvxx0ID5/yC8DMTu3btzgua8IEFpaSm+fPmCZcuW4c6dO7QmqBDwG2veIv/ixYvQ1NREnz592IVQYmIinJ2dISMjA11d3RpHbim1g//FumXLFgQFBaF///7YsGED5z5e0HzcuHE1HHIq79rxzz//YMSIEfDz8+Mc6zx16hRat26NdevWsYtPoHoRZGRkRHf8fzMPHz7E1KlTqeMoIF876T179oShoSEYhuGUZ3ny5AmUlZWhrq6O1q1bw9DQkJa6ERKe7Y2OjkbHjh3Z8ZCQEKioqMDJyYnTlIxXr9/IyIj2oPgJFBYWQkVFBaKiooiOjuZcq6ysRGJiIiwsLDjlWSjCEx8fj8zMTCxevBhTpkxBXFwcJCUlERERAaBal2NiYhAcHEzlXQf4bcLs2bOhp6eHtm3bwszMDO7u7px7S0tL8e7dO3h6etKkFQEZMGAApxEtUN1wkmEYmJub16gZvHHjRsjKymLcuHGca9u3b4ePjw/tiyUEaWlpCA4OZpsZ8jcf19TUxOHDh/Hlyxd4enrC39+/Rl1nSu2ZOnUqFBQUYGdnByUlJXTo0AERERH48uULgOrT+jdu3ICrqyv09fVp7KSOzJ07F/Ly8oiIiMDs2bPh5eUFCQkJJCYmAqg+Lb5z507Y29vDxMSEJnhS/khowPx/FP6g+aVLl9jxqqoqZGZmwsXFhXOMiL5Uaw+/kQ4PD8fcuXPZph9Xr15Fu3bt0Lt3b07m58OHD5GZmUmP3AoBv7xnz54NKSkpWFpaonHjxmwGKD+RkZFQVVVl6/vRQEvtiYqKgrS0NIYPHw4HBweYmZkhPj6evT5r1izIyclh0aJFuHjxIp49e4Zu3brB3t6eOi/1CJW94KxduxaSkpI4c+YMnj59ig0bNsDe3h6WlpasTXn69Cmio6NpaYqfxPXr1yEtLc024gOABQsWoE2bNmjcuDH69u0LBwcHWFlZwdramm4u1wH+E3Bv3ryBmZkZdHR04OzsjFOnTtW4NzExEaqqqhg2bFh9TPePhpexD1Q3dldRUcGzZ8+wdetWSEhIoFGjRmywHKjOLu/WrRumTp1aD7NteKxatQry8vJISkpCeXk5Fi5cCIZh2Czy0tJSbN26le1rQ+2KYDx//pxNDOLf2Fy5ciUYhsGqVavYYCKP0NBQdO3atYb//b1TLJTvk5iYCIZhICEhwfYL4vHo0SO4uLhAVVW1xsY+XfsIzqFDh9CyZUvcvXuX1fnhw4fD3NwcO3bsAFCdVOTo6AgPDw9qS+rI69evYWxsjEOHDrFjvP4H8vLySEtLQ05ODjZu3IipU6dSP5zyx0ID5v/DfCvT/P3797C3t4eGhgbNlhMCfgdk+vTpaNWqFSIjI9lu70B1pnm7du3Qp08fXL9+vcZ30OCWcDx8+BBjx45lgy179+6Fg4MDevbsWeNo8+HDh6kDIyCbNm2CqKgom8Hy5s0b6OjoIC4uDiUlJex9CxcuhJmZGZo0aQIDAwNYWlrSHX/KH0NVVRXKy8vRv39/jB07lnMtLi4OBgYGsLGxQVpaWo1nqU0RjBMnTmDx4sVITk7G8+fP8eTJE7Rr147t9cHj8uXLmDVrFtzc3DB06FBER0fTk291gN8Onzlzhs1izs7OhpmZGTp37ozTp0/XCKikpaVRHReQDRs2QFdXF3l5eXj79i3Gjh2LEydOsNenTJkChmGwd+9e3LlzBykpKejWrRtMTEzo6Qkh+VpegwcPxqZNmwAAR44cgbS0NHuSoqioCED1v4OwsDAacBEQ/nViZGQkrK2tkZCQwI4FBwdDREQEa9eurRE05282SXW89nzLj16+fDkYhkFgYGAN3X38+DGOHz+OnTt30vdmHQkPD4eZmRkKCgo49rl3794wMTFh70tOTqYJcD+Bx48fo3Hjxpx3JlAdv7KysmJPkfP3saE+CuVPhAbM/8fhBc1dXV1x9OhRODs7Q0dHh3WCqKGvHbw6Wjy2bNkCZWVl3Lhxgx2rqqriNM7q0KEDnJ2dvxl4oQjGwYMHoaamBjMzM3z48IEd3717Nzp37gwfHx+22Sc/9MVaO/bt2weGYbB9+3bOuJmZGezs7GBgYAAPDw+2lt+zZ89w/fp1XLt2jTqNlP95eDrKv2gfPnw4unTpUkNvp02bBoZhoKGhgadPn/7WeTYkPn/+jO7du0NXVxft27dHs2bN4OLiAoZh4OnpiaSkJNy+ffs/v4Pab8H5ukyFjo4OwsLCWN/k9evXMDU1hbOzM+Lj41FWVgYbGxsEBASwz1G5146IiAgwDIPY2FgcO3YMDMNAXV0d165d49w3ZswYqKmpQVJSEhYWFujcuTPNTBQS/mBiSkoKysrKYGFhga1bt+LkyZOQlJRkgywVFRVYsWIF4uLiON9BZS44ubm5ePLkCTQ1NeHl5cWpAR8UFARRUVGsW7euRgY5DZQLz65duzgnxIODgyEqKootW7b853NUvwWHZ1eWL1+ODh06sOO8DbcnT56gadOmnDK3/M9Rfgy/LeD9XVxcDEdHR0yZMqVGeTIrKytaH57SYKAB8z+A9PR0uLm5gWEYGiwXAl9fX9bh5hn5SZMm4a+//gIApKamIjIyEqamptDQ0MCBAwcAAOfOnUOfPn3oC/UnEBcXB1dXV0hKStbIJt+zZw+6du0Ke3t72nhSSOLj48EwDPz9/dkFT8+ePdma5cuXL4eqqipsbW2/+TzVccr/Knv27MFff/2F1NRUzmJ+/fr10NTUxLFjxzgnKDZv3gxXV1csWLCALjzrCM/HyMjIwOnTp7Fv3z60b9+e3ZCQlpaGhYUFnJ2dMWnSJNrA9icSGBgIeXl5XL58mc385PkvGRkZ6NSpE/T09KCpqQkDAwNOw1XKj9m/fz8YhmEDKPn5+RgwYAAYhsG+fftq3J+SkoLLly/jwYMHdJNZSPgDLjNnzoSzszOeP3+OCRMmwMnJCTIyMpzSN+/evYO7uzvCw8PrY7p/NLGxsTh8+DCA6prOI0aMAFB90lNXVxfu7u6coPn8+fPBMAy7/qHUjS9fvqBFixawtbXlbMDNmzcPoqKi2Lp1a/1NrgHwvTXLmzdvICMjg9GjR3PGb9y4AS0tLZoAJyT88i4pKeEEx+fPnw8DAwNERUWxyYmFhYXo1KkTVqxY8dvnSqH8CmjA/A8hLS0NEyZMoMcRhcDf359dTPL+u3LlSigqKmLatGkwMTFBz549ERQUhNGjR0NKSgofP37kfAcNKNae78nq3LlzsLOzg6mpaY265Zs2bcKECROonAWkvLycXYQePnwYDMNg9uzZ6NGjB/T19fHixQv23u3bt3MCBBTK/zp5eXnQ0NCAgoIC9PX1MWTIEGzevJm97uPjgw4dOmDv3r14/fo18vLy0KNHDwQFBdGmWT+Bb2UXTpw4EVOnTsX79+9x584dREVFYfDgwRg0aBCVtZDs2rULOTk57Ofnz5+jY8eOOHnyJIDqwOH169cxdepUxMTEsGPbt29HREQE9QsFZOPGjWAYBiIiIpxTEl++fEGPHj2gpKTEnj78XoYt9VWEJzU1FZ06dWJ9kaSkJDZ7//HjxwCAt2/fws3NDdbW1tSuCMiXL18watQoiIuLw8fHBxISEpw6/d8Lmm/evJnaECH5lp3glUV0dHREUlISOz5v3jw0btyYbgQJCb/tjYuLw8qVK7F9+3ZWxnv27IGUlBQGDRqEW7du4ebNm/Dw8ICtrS2120LAL7OlS5fC2dkZ7du3h5+fH/v+HDduHPT09NC1a1dMnz4dtra20NPTo/aE0mCgAfM/EGqAasfXL8YNGzZg/fr1KC4uxvPnzxEQEABDQ0OEhobi4cOHAKqbs9jb23PKhlBqD7/MExIScPz4cRw5coQdO3/+PFxdXWFhYVEj0/xb30H5PvzZtnfv3gVQXfqGYRg0adIEt27dAvCvIx8fHw9tbW2kp6f//slSKEJQUVEBf39/REZG4tatWwgJCYGMjAx69eqFdevWoby8HN7e3rC2toaMjAx0dHSgpaVFawv/QiIjI9GmTRtkZ2d/8zoNbglGVFQUunfvznnvffr0CW3btsXy5cuRnJyMQYMGwcjICBYWFmAYBrt27arxPVTutSM6OhpiYmIICwvDqFGjICcnh8TERPZ6YWEh3Nzc0KpVK05zW8rPYcmSJejRowd69eqFwsJCdvzs2bNQVFSEmZkZNDU1YW1tDXNzc1r6Rkg+ffoELS0tMAyD0NBQANVrR9678eHDh9DX14enpye7MceDrjGFJy8vD8C/vkdmZiY0NTVhZ2fHyTSfPHky7OzsqI9SB6ZPnw5lZWVYWFhAT08P8vLybGPP+Ph4qKuro2XLlmjfvj3s7Oxor6Y6EhAQAHl5eSxduhShoaHQ0dGBra0tW79827ZtGD58ONzc3DB+/HhquykNChowpzRYeI4I7+Xo7u6Odu3aYfv27WymOX+Tm/Lycri4uMDDw4M6MXWE11BVQ0MDEhIScHJyYjO2zp49y2YO/agGLuXb8MoFFRcXY8KECdDW1mYzFHm1WKdPn47379+zz3h4eMDDw4M6i5Q/ihMnTkBaWpot91FcXIzAwEAwDANHR0csWbIE4eHh2L9/P3bt2sU659RJ//lUVVUhMTERrVq1Yu0NT860MZzw8GR49epVts/EnDlzoK6ujkaNGmHy5Mk4duwYAMDLywsTJ06st7n+yRw4cAAMw+Do0aMAqk9uDh48+JtBc3d3d6ipqeHKlSv1Nd0GybZt28AwDJSVlfHo0SMA//rqDx48wL59+7B48WLExsbSBogCwu/bffz4EQMHDkSvXr0gIyODQ4cOsffw1j+PHj2CoqIipk6dWi/zbWisXLkSDg4OePbsGWf87du3UFNTg729PadhNn9TVYpgxMbGokWLFrh69Sqqqqrw/PlzBAUFQUREBHv27AFQXb/89u3buH//Pi2jVUeePXsGbW1t9t0J/HsKyNbWFu/evWPH+WVM5U1pKNCAOaVBwu848jd+++uvv6CpqYktW7awwfIvX77g0KFD6Ny5M4yMjOgudB3ZuHEjFBQUcOvWLbx58wbPnj2DgYEBrK2t2eO2J06cgJWVFVtXkSIYERERsLGxgb6+PuTk5Fgd5y0wDx06BIZhMG3aNHz48AHu7u7Q1NSkuk35Ixk3bhzGjh3LftbV1YW3tzemTZsGT09PMAzDLpIAGiz/lZSUlKBNmzY4fvx4fU/lj4d/syEhIQESEhJYunQpCgoKUFRUhLS0NE4phcrKStjY2NC6oELy5csXJCQkcMYePXr03aC5paUlvLy8fvc0Gwzf8zNiY2PBMAzGjx+PrKys//wOastrB7+sT548idTUVJSUlODDhw8YO3YspKWl2aA5j6KiIrx//57K+CeRkpKCpk2bwsfHhw2a836XAwcOQEREpMbpWhos/zH8MuLJk7c5wU9ubi6mTp0KExMTZGRk1Pgeuu4RnlevXqF169ZsNjlvLfnhwwc0b94cq1evrvEM1W1KQ0KEUCgNjKqqKiIiUq3aCxYsIP379yenT58mhBCyfft20rFjR7J8+XJy4MABUlRURLKzs8n9+/dJhw4dyM2bN4m4uDipqKhgv4PyfU6dOkVyc3MJIYQAIIQQkpaWRhwcHIipqSlRVlYm7dq1IxcuXCDv378nQUFBhBBCXFxcyNq1a0lUVFS9zf1PZsyYMURVVZWkpqYSKysr0rx5c/ZaVVUV6dmzJzl06BBZu3Ytad26NcnIyCAPHjyguk35IzExMSH37t0jOTk5xNTUlDRv3pxs376drFy5kkRHR5N9+/aRPn36sPeLiorW42wbPoWFheTVq1f1PY0/GgCsnjIMQzp37kzGjx9PoqKiyPr160l+fj7R1tYmxsbGpLCwkNy5c4d4enqSgoICMmXKlHqe/Z8FAFJZ+X/t3Xlcj1n7B/DPt8VWlCyhRVGWxJOlMMrIWGKkEUqDsQt5skVk3wZZIkJJNJayFYMxExpkjQgNJsbIGDtNSSLfrt8fvbqfvo/xjJjfJD7vv+reXqfjdt/nXOfc11FDX18fzs7OAAC1Wg0AqFu3LqZMmYIuXbrA3d0dhw4dAgCUK1cOhw8fRmxsbHEVu0Qr3A6/dOkSTp48id9//x3Pnz9Ht27dEBkZiZCQECxcuBCPHj1SzitoRxbgs/yviYhS1wEBARgyZAiSk5ORm5uLqlWrws/PD3369MGgQYOwbds2AMAXX3yBKVOmwNjYGNra2sr/B3ozeXl5Gr+r1Wo0bNgQiYmJOHjwIMaMGYPr168r/y4igv79+6NWrVqoX7++cp5KpfpHy10SFa7rgvqsWLEiUlNTcfPmTWWfoaEh2rZti7S0NDx9+vSV67Df82bS0tKQnJyMzMxMZVvZsmWhVqtx8uRJAPnP5ZcvX6Jq1aqwt7fH/fv3X7kO7236oBRntJ7o/1NAQIBUrVpVdu7cqTHLXESkT58+Ur9+fYmMjJQXL15IVlYWF4krotDQUNHT05NVq1Zp5O3z8vKSdu3aKccVrJq9fft2qVGjhty4cUPjOhz1fzMF9+eLFy/k2bNnsnTpUpkxY4Y4OzuLl5eXUq8Fn9uK5C8E2rJlS2U2AD+Po5LK3t5eVCqVfPrpp6/Nn837+5+xceNG1vU7KDzzavv27RIdHa38PnHiRDEzM5MFCxYoa6lERUWJm5ubtG3blnlBi6hw+yIjI0Pj94K6FBH5+eefpV+/flKlSpVXcjqzjVI0he9vf39/qVOnjujp6cm//vUv6dmzp2RkZIiIyDfffKMsVH7//v3iKu4HY9asWWJsbCxHjx7VyA8vkp+i5d///reoVCpp2LChWFtba9z/9OYKPw+io6Nl9uzZEhAQIKdOnRKR/EVtK1SoIF988YX88MMPcufOHenatauEhIQo5/H5/WZ27dolffv2FQ8PD1m8eLGy/dSpU2JnZyfTp0+X33//Xdn+008/iY2NjcaXWfTmIiMjxcbGRoyNjaV27dpy8OBBZV94eLjo6OhIeHi4si03N1fs7Oz41Rt98Bgwpw/S5cuXxcbGRvbs2aOxvXADsW/fvmJgYKDkBhXhJ0RFNXz4cLGyspKVK1cqQax9+/ZJ2bJlZc2aNRrHbtmyRRo1aqTkvqU3V7iBnp2drbFvxYoV4uTkJF5eXpKWlqZsT0hI0DiOAS4qiQqeyRs2bBBbW1s5c+aMxnYqPnymFF3hZ3lycrLUr19f2rdvrxGknTRpkpibm8uCBQskMzNTHj16JPHx8czp/A6+/vprad68uXTp0kUWLVqkbP/voLmrq6t06tSpOIr4wSi4x4OCgsTIyEji4uLkwoULsmLFCmnRooW0bt1aWbR88+bNolKpNIKJVHSPHj0SJycnCQsLE5H8xSaPHj0qw4cPlxUrVijt7gMHDsiaNWv4LPkb+Pn5Sc2aNeWLL76Q3r17ayzIfPnyZWnQoIGYmpqKiYmJNGnShAMURRQaGioVKlQQb29vcXd3l1q1asmmTZuU/TNmzJB69eqJr6+vHDx4UC5evCgdOnSQVq1acZDzLaxevVpKly4tq1evlosXL8qnn34qDg4Oyv6srCyZMmWKqFQq8fLykpEjR0rbtm3FxsaGzxH64DFgTh+kw4cPi6GhoVy9elVENIMrhQOOM2bM4Ej/W8jJyVF+Hjp0qDRo0EBCQkLkjz/+kOzsbPHz8xNLS0tZsWKFPHnyRG7duiWff/65dO7cmYGudzBv3jxp06aNdO3aVVauXKlsDwkJkU8//VS6desmx44dUxqNrGv6UNy6dUuqV68u8+bNK+6iEL2zSZMmyYABA8TW1lZKly4tTk5OGgtqBQQEiIWFhUyZMkUJLopwtvObKlxPISEhYmRkJF9//bV4eHiIra2tDBkyRNlfOJB18+ZN1vFbKhjMFMlvI/bs2VNmzJihbMvNzZXvvvtOmjZtKpMnT1bqef/+/Qy4vIO8vDy5e/eu2NjYyIIFC2THjh3i5eUlrVq1kn/9619iZ2cn06ZNe6Wvw75P0RXcpzExMVKjRg1JTEwUEZE9e/aISqXSCOjevn1b9u/fLzt37uQARREVzGaOiYkRkfxc2a1atZItW7Yo64+JiCxdulTatm0rKpVKGjVqpPFFLZ/jb27t2rVSqlQpjQmEcXFx0q1bN9m9e7f8+OOPynoTu3btks6dO4u7u7sMGzZMuaf5PKEPGQPmVOIVDgoW/Hz+/HmxsLCQXbt2KfsKHuaRkZGyfft2jWvwQf/mCtf3+vXrZf78+VK6dGkxNjaWVatWyfPnz+X333+XqVOnStmyZcXExESsra2ladOmbMgUUeF6CgwMlEqVKom/v794enpKhQoVJCAgQNkfHh4uzs7OYmJiIk5OThqpWYg+BMHBwVKpUiX56aefirsoRG8tJCREKlSoICdOnJBbt25JQkKCNGnSRFxcXDQ6rCNHjpRu3bpx4PMdxMfHy4IFC2T37t0ikp+WZfXq1WJlZSWDBg1Sjvvv9yXbKEWzevVqUalUcuXKFWVb+/btxdPT85VjBw8eLO3atXuljhlMfDOvuzcnT54sJiYmoqenJxMnTlQWuO3Ro4f4+Pj8k0X84MTFxWk8h5cvXy4DBgwQkfwFPfX19SU0NFRERP74449XUk+KsJ/5pnbu3CkqlUqCg4M1tjdu3FiaNWsmNWvWlPbt20tSUpKI5Nf3uXPnJCUlRfm/wWfJm/v111+lRo0a8sknn2hsd3Z2lmrVqompqalUr15d7O3t5fr16yIir3wtwfqmDx0D5lSi/XfDsWD2eEZGhjRr1kzatWunsSJ5bm6uuLi4yNChQ//Rcn6Ipk+fLoaGhrJp0yaJjIwUNzc3qVKliqxatUqZgX7t2jXZtm2b/PDDD5xh8Q7OnDkjISEh8sMPP4hIfgMxJCREtLW1NYLmN2/elPPnz7PRSB+ka9euyVdffcVgFpVogwYNEnd3d41tx48fF0tLS2nVqpVG0LzgvcmgedElJCSIqampVK5cWSNFWUZGhoSGhkqdOnU0ZprT21m9erXo6OhIbGyssi0vL0+mT58uzZs3l9OnT2s8s1esWCGOjo4aM0XpzRSux507d8ratWslKChIyQv/888/awxaiOQPXEycOPEfLeeH5NGjR2JhYSH16tVTnsPTp08XNzc32bZtm5QvX17ji89vvvlGhg0bpvFlEL25nTt3ioGBgYwZM0Z+/fVXERFxd3cXS0tLCQsLk7Vr14qVlZXY29v/aZobtg+LJj09XcLCwsTExEQZWPPw8JB69erJuXPnJDMzUyIiIqRGjRoyffp0UavVGn1Ltk3oY8CAOZVYhV+KixYtEi8vL6lbt64EBgbKzZs3JS0tTUxNTcXZ2VkCAgIkNDRUPv30U2nYsCEDie8gLy9PHj58qKRhKax///5iYGAgq1atkocPH75yLmdYFN3hw4dFpVJJpUqVNDr9WVlZEhISIrq6ujJlypRXzmNd04eIizNTSVXQZvHx8REXFxcRyb+fC+7ltWvXSrly5eSLL76Q/fv3K+exQ/p2bt68KdOmTRMjIyPx9fXV2JeRkSFhYWFSoUIFpnl6BxEREaKtra0xyCOSv8jkgwcPxNraWlxcXOTHH3+UZ8+eSUZGhrRt21Z69+5dTCX+MIwdO1aMjY3Fzs5OqlevLubm5hITEyPPnj0TkfxJFadPn5bPP/9cbG1t2ed5B3l5eXLs2DGxtbUVOzs7ycvLk8TERKlfv76UKVNGlixZohz75MkT6dKli4wcOZLP7Xewbds2MTU1lVGjRkmHDh2kUaNGGrP29+7dKyqVSmNRSiq6grZHRkaGrFu3TqpWrSrGxsbSuHFjuXv3rnKcWq2Whg0b8ksV+mhpgaiE0tLKv30DAgKwcOFC2NvbY+zYsfj666/h6+sLc3NzHDp0CJaWlti7dy+++eYbmJqaIikpCTo6OlCr1cX8F5RMKpUKurq6EBFoa2sDAHJycgAA69atQ926dbFs2TJEREQgKytL49yC4+nNWVhYYPr06Xj27BlOnjypbNfT00O/fv0QHByMuXPnIjw8XOM81jV9iFQqFQDe3/T+y8vL0/i9oM3Spk0b/PDDD9i6dStUKpVyL5cqVQrOzs64desWNm/erJxXcM/T6/13XQOAmZkZRowYAV9fX+zduxfTp09X9lWoUAEeHh7YsGEDxo8f/08W9YNx7NgxDBo0CGPHjkXnzp2V7T179sTixYtRuXJlHDhwAHfu3MGoUaNQp04dtGvXDg8ePMC6desAACJSXMUvsaKjo7FhwwbExcXh8OHDuHXrFuzt7eHn54djx44BAI4ePYoRI0ZArVbj7Nmz7PO8A5VKhRYtWiAsLAzPnj1Dy5YtYW9vj+7du6N8+fJ49uwZUlJScOLECfTs2RO3bt1CUFAQVCoV7+8iKqivHj16YPHixYiOjsapU6ewZMkS1KxZUzmudOnSqFu3LoyNjYurqCXa4cOHsXjxYvj4+CA7OxsVKlRAjx49sGDBApQrVw516tRR6jYvLw9ZWVmoUKECLCwsirfgRMVEJXyaUwmWlJSEPn36ICIiAi1btsSZM2fQokULRERE4KuvvlKOy83NRU5ODsqXLw8AePnyJXR0dIqr2CVKXl6e0tEvrEOHDnj69KnSQM/NzYWuri569eqFI0eOwNnZGRs3bmRnvwheV9fp6ekIDAxEUFAQgoKCMHz4cGVfVlYW9u/fD1dXV97TRETvgcLP8oMHDyI9PR2lS5dGhw4dULp0afj7+2Pp0qUIDQ2Fk5MTKlasiP79+8PV1RXGxsb44osvcPHiRTRo0KCY/5L3X+G63rBhA3755Rc8fvwYffv2hb29PdLT0xEcHIyoqCh4enpi5syZr1xDrVZzEK6IsrOz0bFjRwD5E1c6deqEXr164ezZs9i/f78S4Hr06BESExPx008/oUqVKujduzd0dHTYDn9LixYtwp49e7B//36oVCqlDl1cXPDgwQMkJSUBAE6ePAkHBwdoaWmxrosoMTERjx49QqdOnZS6e/nyJc6ePYtevXrB1NQUR44cwbRp07Bnzx4kJyfDwcEBFSpUwN69e6Grq8tnyht6Xb8HAHbt2gUfHx9069YNw4cPh42NDQCga9euyM3Nxd69e197Lv25yMhIzJ8/H926dUPt2rUxaNAgZV96ejp27dqFiRMnwtXVFWvWrAEAdO7cGXfv3kViYiKfI/RxKs7p7UTv6uTJk9KkSRMREdmyZYvo6+srueSePHki+/fvl6ysLI1z+Jncmyuc9ubcuXNy9epVuX37toiIpKSkSLVq1aRbt24ax/bq1UuOHj2q/M76fjOF6zokJER8fX2lY8eOsm3bNrl37548e/ZMpkyZIuXLl5dVq1b96TX42S0R0fvDz89PzM3NxdzcXCwsLMTCwkJZtHb69OlSrlw5Zb+NjY3k5OTIuXPnxMrK6k8XjqPXGzdunFStWlXat28vDg4OUrp0aZk/f748ffpUHjx4IDNnzpQGDRrI6NGji7uoJV5BWyMrK0vatm0rLVq0kJYtW0qDBg3kzp07ynGvyyfMlFpFV9CWDggIEGtra2V7wdpNZ86ckcqVK0tycrLGeczpXDTx8fGiUqlEpVJJixYtpH///hIbGytpaWkiInLq1Cmxs7OTVq1aiUj+AoiHDh2StLQ0rh9URIXvzZiYGFm9erWsWrVKHj9+rNzv0dHRYmJiIiNHjpTLly+Lq6urWFtbK/nLeX+/uQ0bNkjZsmVl8+bN8ujRI2X7lClT5PLlyyKSn85p3bp1Uq1aNfH29pZu3bpJnTp1lPrms5s+RgyYU4lx584duXDhgmzYsEEuXrwojx8/lvPnz0uVKlUkLCxMDAwMNHJqHzhwQNzd3eXnn38uxlJ/GCZMmCA1a9YUQ0ND6d69u+zevVtERPbs2SM1atSQunXrSpcuXaRJkyZibW2tvFDZkCm6CRMmSJUqVWT27NkyePBgqVWrlgwcOFByc3Pl9u3bMnXqVDE0NJSFCxcWd1GJiOg1IiIixMjISBITE+X27dvy008/SadOncTExERu3rwpIvmD/rt375aYmBjlvTlu3Dixs7P703VA6M999913YmxsLOfOnVPaHfPmzZOKFSsq7cLffvtNxo0bJ19++SUH8t/Sn9VbVlaWdOrUSXR1dSU0NPR/Hktv7nXt56tXr0qVKlVk1KhRGtsTEhKkTp06cu3atX+gdB+ua9euScuWLaVZs2bi4uIivr6+YmhoKLVr15auXbvK0qVLZf369WJiYiLt2rV75T5nv+fNFK43f39/MTY2lrZt24qRkZG4uLhIXFycRtC8Zs2aoq+vLw0aNFCCtxyYeHNXrlwRW1tbCQoK0tjevXt3UalUYmxsrBE0X79+vZQpU0bq1q3L+qaPHgPmVCLs2LFDOnfuLNWqVZMKFSpI2bJlpWvXrnLixAkZOXKkqFQqmTFjhnJ8Tk6OdOnSRbp3787Gy1so3JA5ePCg1KpVSw4dOiTr168XDw8PadasmcTExIiIyL1792TcuHEycuRIGTt2rPJC5Sh00f34449iZWUlZ86cEZH8utfR0ZGNGzcqxzx69Eh8fX3/tKFORETvh0mTJsmXX36pse2PP/4QJycncXR0fKXzeenSJenfv78YGRm9MkuU/mPq1KnKLP0C0dHRYmtrK+np6Rr1WjDAXDDr+dGjR8p7k+/Poinclv7999/l6dOnyrasrCz57LPPpHnz5hIbG6v8G7CO307hut63b5+sWrVKdu3aJRcuXBARkRUrVkjt2rVlyJAhcvPmTblw4YK4urpKmzZt2Of5G6Smpkq3bt3k888/l+TkZHn06JEcOHBA3NzcpHXr1lKmTBkxMzMTlUr1ysAF/bXC92hQUJCYmpoq/Z6oqChRqVTSpk0b2bdvn/IMiYqKks6dOyvPFgZv30xBXe/Zs0fq1KmjBMVFRObPny/169eX48ePi6urqxgbG8ulS5dEROTx48eyd+9epS/P+qaPGQPm9N4LCwuTihUryqJFi+TAgQOSnp4us2bNknr16kndunVl2rRp0qtXL6lZs6ZERkZKUFCQdOjQQWMUmg3ItxMTEyPDhg2TefPmKduSkpLkq6++kqZNm0p0dPSfnscX61+bOXPmK53+b7/9Vlq2bCki+QGA8uXLa6QYOnLkiIiw009E9L4bNmyY2NjYKL8XdDwjIyOlbt26GqkrsrOz5dChQ9KnTx8lKEavunjxokbQpEBkZKTo6+tLenq6iIg8e/ZMRPIDu9WqVZO4uDiN4/nefHtTpkyRJk2aSO3atWXFihWSmpoqIvltFGdnZ3FwcJCdO3eyHfg38PPzE2NjY7Gzs1PSNkVFRYmIyPr168Xc3FwMDQ3FyspKPvnkE/Z5/kY///yzdOzYUdq3by/Hjx9Xtr98+VK+/fZbWbZsmfTq1Uupc/pr/v7+ymCwWq2Wx48fy5gxY2TNmjUiIrJ9+3YxNDSUefPmSb169aRp06by3XffvXI/89ny5jIzM0VEJDAwUMzNzZV3o4hIYmKi8iXbgwcPxNnZWSpWrCiPHz/WuAYnwNHHjgFzeq+FhYVJqVKlZMeOHa/si46OlmbNmknr1q0lOjpaRowYIWZmZuLs7CyDBg3iKPQ7+uWXX6R169ZiaGgofn5+GvuSkpKkX79+4uDgIGvXri2mEpZcBw4cEC8vr1fuzbVr10qLFi0kLi5OKlSoICtWrFD27dq1S0aMGKHkkBdhp5+IqLi9LnXK/v37pUGDBhIUFKTxrN+3b5/Ur19fSctS4OXLlxqdWdL03++7HTt2KIGsZ8+eib29vbRr105j3Zpr165J7dq15ejRo/9oWT9UmzdvFlNTU9m4caMMGjRIbGxsxMfHR1JSUkQkP2jerl07sbCwUAb46e1s2bJFqlSpoqwJdP78eRk3bpzUqFFDtm/fLiL5+bMPHz6skYqIfZ6/T2pqqnTs2FE6duwohw8ffu1xDJr/tXPnzomDg4O0bNlSmSz0/PlzSUhIkAcPHkhKSopYW1vL0qVLRSR/ApGurq40bdpUjh07VpxFL7FiYmJk8uTJIpI/qKxSqf7n12vLli0TFxeXV9Z+I/rYcWlhem8dOnQI3t7emDx5Mtzd3SH5Azx4+fIlAMDT0xN9+/bF+fPnoauri5CQECQlJSE+Ph7h4eHKquZc0fnt1KpVC9OmTYO9vT1iY2Nx4MABZV+TJk0watQoGBsb49ixY8VYypLps88+Q2RkJHR0dLBr1y6cOHECQP49fffuXXTs2BErVqyAj48PAOD58+cIDQ1FRkYGqlWrplxHpVIVS/mJiAhISEhAjx49cOTIEWWbiAAA7O3t8cknn2DXrl34+uuvkZGRgV9//RXBwcGwsLCAqampxrW0tbVRpkyZf7T8JUlBvarVavz2228YOHAgFi9ejKSkJJQpUwbTp09HZmYmPvvsMyQkJOCHH37A6NGjUblyZbRo0aKYS18y5eXlvfL7uHHj0Lt3b4SHh2PkyJE4ceIEQkJCcOnSJejr6yMmJgaurq745JNPiqnUJU/BvQ38p84vX74MOzs7tGrVClpaWmjUqBF8fHzQuXNnhIeHIz09Hbq6umjdujXs7OygpaUFtVrNPs/fyNraGsuXL4dKpcK8efNe29/R1dX9h0tW8tjZ2WH27NkwNDTEgAEDkJKSglKlSsHBwQGVK1fGqVOnYGxsjC+//BIA8OTJE/To0QONGzdG8+bNi7n0JdOePXvw448/AgDatGmD+vXrw9vbG2lpaQCAFy9eKMc+f/4ccXFxqFOnDvT09IqlvETvKwbM6b1lYmICR0dHnD17FgkJCVCpVFCpVNDR0VEalL6+vjAzM1OCuYaGhsr5IsKG4xsq3Ckq3HD/7LPPMGHCBNStWxeBgYGIj49X9jVu3BiLFi3CmjVr/tGylmQTJ07ExIkTAeQ3sFNSUjBu3DgsX74cp0+fhp6eHpYsWQITExNERUUhPj4e27Ztg5ubG27evIn169dDpVJp/BsREVHxqFq1KkQEgYGBSjBFpVJBrVbDwMAAc+bMQcOGDbF161ZUqVIFXbt2xb1797Br1y6oVKpXApL05/Ly8qClld9lUavVMDMzw+7du3Hx4kUsWLAAKSkp+PzzzxEYGIgKFSrA1dUVY8eORU5ODhISEqCtrQ21Wl3Mf0XJIiJKna9btw4zZszA3r17Ubp0aeWY4cOHY/DgwTh16hRWrlyJ8+fPo3z58ggODmadF0HhNl1BnVesWBG3b9/GnTt3lH2WlpZo164dEhIS8Mcff7xyHW1t7f/3sn5srK2tlft59OjRuHDhQnEXqcTJzc0FAHTo0AH9+/eHgYEBvL29kZqailKlSkFEcO/ePWRkZODu3bvIzMxEdHQ0HBwcsGbNGj5Liqigrlq0aKHUvbm5OQYMGIBbt25h4MCBSEtLQ6lSpQAAqampcHNzw61bt7B48WIAYD+TqBCV8H8EvceuXr0KX19fiAimTJkCR0dHAPkPcpVKhczMTDRt2hRfffUVpk6dWsylLZkKd0QjIiKQmJiIUqVKoUWLFspI/759+xASEoIXL15g0qRJcHZ2fu016M9lZGTA19cXqamp+OKLL+Dv7w8AiIqKwrJly2BlZYUJEyagUaNGOHz4MMaOHYuHDx/C2NgYlpaW2LhxI3R1daFWq9kpIiJ6TxRup0ydOhWtWrUCkB8k0NXVxYsXL/DixQuEhISgY8eOaNiwIbS1tfkF3Bsq3L4ICQnBnTt3MH78eBgYGODEiRPo06cPmjRpgqlTp6JRo0YA8mfnGhgYoFq1atDS0mJdF1HhOp80aRJCQkJgY2OD5ORk1KlTB5s2bULDhg2V40NDQzFnzhyMGjUKfn5+Shud/tqePXuwa9cu5OXloWXLlhg8eDAAIC4uDkOGDMH48ePRp08fZUJQUlISBg0ahG3btsHa2roYS/5xuXz5MsLDw7Fw4UL2d4qg8LNg3rx5OHPmDK5du4aLFy+iRYsWCA8Ph42NDa5duwYnJyeULVsWarUahoaGOHPmDGfvv4PExES0b98eBw8eRLNmzQAAs2fPxtq1a3H37l24ubnh3r17eP78OVQqFQ4fPsx+JtGfYMCc3nt/1hkteAEnJydjzJgxCAgIQPv27dlIfwf+/v5Yv349evTogXv37uHatWvo0qUL5syZAwD4/vvvsWrVKvz2228IDw9HkyZNirnEJc/9+/cxd+5cJCcno127dsogT3R0NBYvXoy6devCz88PdnZ2AIDr16+jYsWKMDQ0hEqlYqefiOg99L8G9+/cuYNBgwbB3NwcoaGhAMAO6Rsq3KYbP348Nm/ejGnTpqF9+/aoVasWgPy0OP3790fTpk3h5+cHBwcHjWtwQP/tXb58GStWrMCAAQPQrFkzREdHY/Xq1ahUqRJmzpwJW1tb5didO3fC1dWV93URhIWFYfz48ejZsyeuXbuGrKwszJw5E59//jmA/K8S16xZg7Fjx6J169YwMTHB8OHDkZOTgx9//JH3dTHhM6XogoODMXnyZMTGxsLS0hJxcXGIjo7G8+fPsWbNGjRs2BC//PIL4uPjoaWlhX79+jG1ahF9//33OHv2LDp06IBKlSpBrVajY8eO2LhxI1q2bKkcd+zYMeULrapVq+KTTz7BwIEDOZBP9Dr/TKp0oneTmpoqLi4u0rFjR2UhodzcXOncubN06dKFK8K/o7Vr14qVlZUkJiaKiMimTZukVKlSYm5uLqNGjVKOi42NFT8/P9Z3ERVeYfzAgQPi4eEhVlZWsnjxYmV7VFSUNGvWTPr27SunTp165RqscyKi91fhdkrBIpN3796V1q1bS+3atbkwXBH89+KnERERUq1aNaWNIpK/CGhGRoaIiJw4cUKsra2lffv2cvny5X+0rB+q7du3i5mZmTRt2lTu3bunbN+0aZM4OztLt27dlMU+Cyvc3qHXCw8PF21tbYmJiRERkVu3bkn9+vVl586dkpOToxw3e/Zsadq0qZQpU0YaNmwozZs3V54lbBfS+y4vL09yc3PFy8tLRowYobFv586d0rBhQ2nVqtWfPrf5LHkzBe/Cjh07io2NjVhZWYmenp64uLiISqUSV1dXOXHihJw9e/Z/Xof1TfTnOMOcSoyCGVxaWloICAjAkiVLcOXKFSQnJ0NXV5cj/u9g7ty5yMnJwezZs7Fr1y4MGDAAkyZNwqNHjxAaGgofHx9lpnkB1nfRjRs3DufPn4eWlhaSk5NRrlw5DB06FAEBAQCALVu2ICgoCJUrV1ZmnBMRUclQ0E5RqVQYPnw4li9fjlu3bimLk3P21l/z8vJCr1694ObmpswwHz16NNLT0xEZGYlLly4hISEBYWFhyMjIwPz589GjRw/Ex8dj9erViI6OZtvkb7Br1y6EhoYiISEBJ06c0JhNHhUVhYiICLx48QLr16+HpaVlMZa05NmyZQu8vLywfv16fPXVV8r2Zs2aoVy5cvjjjz9Qs2ZNhIWFoXr16rh+/ToePnwIEYG9vT3TDNF7raB/KIW+EBo8eDBu3LiB77//XuO+9fPzw5IlS1CrVi388MMPqF27dnEVu8QreCb89ttvuHLlCh4/fowpU6bgl19+Qa1atfDgwQPUq1cPBgYGsLGxwcCBA5U0ZkT0enzTUolRsPDKmDFj4OzsjFq1auHixYvshBZRXFwc9u/fj8zMTDg7O6NXr16YNGkS0tLScPv2bUyZMgUBAQHw8/NDcnIywsPDERQUBENDQ/j5+SnXYYe0aLZu3YqIiAjExcWhYcOGyMzMxIQJExAbGwttbW34+/vD09MT2dnZOHbsGHNTEhGVMAXtlNGjR8PNzQ316tVjsLyILC0t0alTJwD5eeBLlSoFMzMzREVFwc/PD/Hx8bC0tISrqyvu3r2LgQMH4tNPP0Xbtm3Rtm1bABzQL6o/qy83NzeUL18eWVlZ6NevH9avX6/kLffy8kJ2djbOnz+PmjVrFkeRSzR9fX0AwJUrV/DkyROUL18e3bt3x4MHDzB+/HhkZ2dj+fLl8PDwQEJCAmrVqqWkIALy/734LKH3UVRUFL7//nv4+/vDzMwM5cuXBwA0adIECQkJiIuLw2effaYsHmxjYwMXFxe0bNkSFhYWxVjykq8gHZaZmRnMzMwAAMePH4eOjg4mTJiAO3fuIDExEUePHsWjR4/QoEGD4iwuUYnBGeZU4ly5cgUrV67EkiVLmN+siNasWYOAgAA4OjoiLS0NFy9exOrVqzFo0CAAwMGDBzFixAgcPHgQpqamOHPmDAIDA+Hu7o6ePXsyN+U7CAwMxObNm3HmzBnlfr116xaGDx+OpKQkjB8/HmPGjNE4h51+IqKSh+2Uovvv992qVasgIhg4cCDu3LmDiIgIfPvttxg4cCA6dOiA+vXr4/Dhw5g2bRq2bduGqlWrFmPpS67C9f7jjz8iJycHubm56Nq1KwDg0KFDCAwMxKNHj7B27VqNmeZ/dg16vZcvX0JbWxsqlQo7d+6Eu7s7/P39cfnyZfzyyy/YvXu3EjT85ptv0L9/fyQkJCgLCRO9zzIyMtC0aVNkZmbC2NgYTZs2RevWrTFw4EAAgLu7O1JSUjBr1iw4OjqifPny6NevH+zs7DB9+nSoVCqu7/E3Cw0Nxbx583D27FkYGRm9sp/1TfTX2HqnEqdevXoIDg4GAHZCiyA8PBwjR47E5s2b0b17d6SkpKBTp07YtGkTevXqBT09Pejr6yM7OxvR0dHw8vLCtGnTUL16dXh6erIh85YKOpJVq1aFWq3GrVu3YGFhgby8PJiamiIgIACdOnVCcHAwDAwMMHDgQOUzRnZAiYhKHrZTiq7g0/2Cd+bevXtx+fJl6Ovro1evXpg9ezb8/f2V2bkvX77E/PnzUaFCBVSpUqU4i16iFbQzChZVLVu2LO7cuYMWLVpg/vz5aNOmDdRqNZYsWYKhQ4ciJCQEjRs3/tNr0OsVzCQHgPPnz+OLL77Atm3b0LNnT5QuXRrHjh2DhYWF0v6rVKkS6taty4EgKjH09fXh4eGBmjVrwt7eHvHx8Rg7diy+++47tGnTBlu3bkXPnj0RHByMYcOGoUaNGsjLy8P27duhUqkgIuxj/o1EBPXq1UNubq7yfi3oxxfMl2V9E/01tnCoRGMn9M0cOnQIQ4cOxeTJk9G9e3cAgK2tLcqUKYP79+/j6dOnSE9Ph4ODA9zd3RESEgJ7e3vcu3cPq1evZkOmCPLy8jR+L+hIOjg44ObNmwgODkZ2drayPTc3F05OThgzZgz69+8P4D+BAyIiKtnYTvlreXl5ynvv119/BQDs2bMHjo6OmDt3LjZt2oSsrCzo6+sjKysLMTEx6NChA+7cuYOYmBioVKpX3r305sLDwxEZGYndu3fj8OHDuHjxIh48eIBRo0YhNTUVn332Gf79739DRLBy5criLm6JEx8fj0GDBiEnJwe+vr7o1asX0tPT0b17d+zZswfPnz9HVFQU7t27p/w/WL16NaysrJjTmUoMbW1ttG7dGhMmTICOjg78/Pxw9+5dNGjQAL6+vmjfvj0cHBzQu3dvhIWFYfLkyfjpp5+go6MDtVrNvs/fTKVSoUWLFtDV1cXJkycB/CdArlKpWN9Eb4iteKKPgImJCRwdHZGUlIQzZ86gWbNm6N69O27fvo1WrVqhe/fuyMzMhJubGxo1agR3d3cAgKOjI7S1tTlD7g0V/iz52LFjuHfvHkxNTWFtbQ0bGxusW7cOHh4eyM7OhqurKywsLDBv3jxYWlri3//+N2fxExHRR6Xwe3PWrFnYs2cP5syZgw4dOiAyMhJ9+vTBggULAACenp549OgRLl68CGtra4SEhDDlTRH98MMPcHBwQMWKFZXZzJcvX8ann36KJk2aKG2Qw4cPo2nTppg+fTqioqLg4uICIyMjNGvWrLj/hBInNTUVt2/fhr29PW7fvo3ExERUrFgRarUanTt3xvbt29GjRw+ICCZMmICBAwfi6tWrSElJgZaWFlPeUInh4uKCvn37IjQ0FCEhIShTpgy2b98ONzc31K5dGydOnMCePXuwadMm9O7dGwDTgvx/e/r0KdLS0oq7GEQlFnOYE30krl69Cl9fX2hrayMjIwPZ2dmIjIyEjY0NUlJSkJqaisDAQNy4cQOdOnVCZGQkADZk3lTh1eAnTpyIHTt2ICcnB+bm5jAzM8OSJUtQo0YN7Nu3D35+fsjIyICOjg6qVKmC48ePQ1dXV+MaREREH4vJkycjPDwcYWFhsLW11ZhZ27dvXyQlJWHixInw8vLCixcvUK5cOQ4yF1FYWBjGjh2LRYsWwcvLCwYGBhAR9O7dGw8ePMD+/fsBADk5OShTpgx27NgBX19fHD9+XGNxTwZwi65Xr17YunUrOnXqhA0bNsDIyEiZVaulpYXY2Fh4eHhAW1sbderUQVJSEhcLphJp7dq1WLduHb799lu0a9cO5cqVw3fffYcKFSrg7t27OHLkCNzd3Xlf/0M2bdoET09P1jfRW2LAnOgjcvXqVYwYMQKnT59GWFgYPDw8APyn8/Ps2TOkpaXB2tqaHdC3FBgYiKVLl2Lr1q1wdHSEn58fQkJC4OjoiLVr18Lc3By3b99GVlYW0tPTYW9vDy0tLXaKiIjoo3TlyhV0794dgYGB+Pzzz5Xtubm50NXVBQB89dVX+Pbbb7F582Z07twZADjI/BZGjBiB/fv3Y+zYsfD09ISRkRG+//57uLu7Izg4GIMHD1aO3bp1K+bOnYtDhw6hYsWKxVjqkqfg3szNzYVarUZoaCj++OMPHD58GNWqVcO8efNQs2ZNvHjxAqVKlQIA7Ny5E4GBgTh8+DCD5VSiOTg44MyZM2jdujViYmL+dMFJ3t//LNY30dthwJzoI/PLL7/Ax8cHWlpaCAgIgKOjI4BXX6SctfVmCs+0unv3Lry8vODj44MePXrg+++/R8+ePeHl5YXExERUq1YN69atQ/Xq1TWuwbomIqKP1ZEjR+Dm5obTp0/DyspKIxD+7NkzlC1bFgAwc+ZMTJkyhe/Lt/D8+XOULl0aAODt7Y1jx45hxIgR6N27N0qVKoVp06Zhx44dGDduHPr164eMjAx4e3tDRLBnzx4OTBRB4XZh4fsXAEJCQrBlyxaYmppi/vz5MDc3BwAcPXpUaY8DDG5RyVTw7N64cSMWLFiA9evXo2nTphzcJKISi9/TEX1kateujeXLl0NEMHfuXBw7dgzAqwuTsUP610RE6RQdPHgQRkZGmDRpEpo3b47ExEQMHjwYixYtQlhYGJycnBAXF4fOnTvj/v37GtdhXRMR0ceg8Dydgp8NDQ1haGiIS5cuAYCSagUAtm3bhh07dgAApk+fDm1tbWUfvRkRUYLlkZGRqFWrFq5du4ZZs2YhKioK2traGDNmDPr06YPx48ejXr16cHZ2xt27d7Fz504uqlpEBe3C+fPno3PnznBzc8OqVasAAD4+PujVqxdu376N0aNH4/jx4+jYsSMmTpyo8X+DwXIqiQqC4s7Oznj06JGS5onBciIqqRgwJ/oIWVtbIzg4GNra2hg9ejQuXLhQ3EUqcQrPlpg6dSpGjRqFGzduoEOHDjAzM8N3332HNm3aYMCAAQDyBypcXFzQuXNnVKpUqTiLTkRE9I/Ly8vTCJzk5OQAACwsLFC5cmUsX74cKSkpAKAsOB4VFYW4uDiN63CQuWgK6nzGjBkYPXo0zMzMEBYWhhYtWmDatGmIiIhApUqVMGvWLFy8eBFLly7FihUrcOrUKSU1CHOW/7XCgwoLFy7EokWL0Lx5c5QtWxYTJ07E5MmTAeSnxenbty/++OMPeHh44NmzZ4iPj2dQkT4YJiYmmDRpEhYtWqQMhBIRlUQcvib6SFlbW2PhwoUIDw+Hra1tcRenxCno2Ny4cQMpKSkIDg5GnTp1lP0PHz7ExYsXkZubi1KlSiEhIQHt27fHmDFjADANCxERfTwKp6lYvHgxkpKScPbsWQwaNAi9evXCjh070KpVK/j6+qJly5aoWbMmNm/ejMePH2P37t3FXPqSTUTw+PFjbN++HXPnzsWXX34JID8v/IABAzBx4kQAQM+ePVG7dm2NBVfVajVnO7+hgvs7KSkJenp62Lx5Mzp06ICMjAxs2rQJvr6+AIC5c+di0KBB6NChA9LT02Fra8u1bOiD07lzZ5w5cwb16tUr7qIQEb01vpWJPmL169fH4sWLAWh2Zun1Cs8sX758OZYsWQJjY2NYWloC+E89Ojs748yZM2jWrBn09PTw9OlTbNmyRbkGg+VERPSxKGhfBAQEICIiAv7+/mjTpg38/f1x/PhxxMbG4tChQ/j666+xd+9e6Ovrw8LCAvv374eOjg4Hmd+BSqWCrq6uRtsjJycHZcqUwbp163Dp0iUsW7YMT548wfDhw6Gvr6+cyzovmiNHjqBNmzYwMjLCzp07AQAGBgbo168fAGD06NHQ0tLC7NmzYWZmBjMzMwAcmKAPT+3atbF+/XolxRafJURUEvHNTEQAwGD5G0hISMDp06cBAMOGDUOPHj2wdOlSJCYm4sqVK7C0tFTq0c3NDSKC5ORk5OXlYdasWez0ExHRRyspKQmxsbGIjY1Fy5YtcebMGTx58gTdunUDkB9gWbt2LXJzc5GTk4Py5csD4AKIRfVnEyAqVKgAExMTfPPNN/D29kaZMmWQm5sLXV1dWFpa4siRI0hOToaenl4xlfrDYGFhgenTpyMwMBAnT55UFvLU09NDv379oKWlhREjRqBmzZoYPHiwch7bhfQhKphgxPubiEoqtj6JiN7Ahg0bMGfOHLi4uMDGxgblypVDuXLlkJSUhGbNmmHatGkwNzdHgwYNAOQv2NSjRw/06NFDuQaD5URE9LF6+fIlypUrh5YtW2Lr1q0YNGgQli9fjq+++gpZWVk4efIkWrZsCT09Pejq6gLI/yKLwfI3VzhYnpycDH19fejp6aF69eoICgpCu3bt4O7ujpiYGKU9olKpsG3bNrRs2RIqlUrjSzp6vT8bmDA3N4evry9ycnIwZcoU6OnpYfjw4QDyg+Z9+vSBsbExXF1di6PIREREVARsgRIR/YUNGzbA29sboaGh6Natm/K5cmBgIJycnJCUlAQ7Ozt4e3sjLCwMNjY2AF7tTDFYTkREH4O7d+/iwYMHOH/+POzs7GBiYoKyZcvit99+w5o1azB+/HgsWLBACSaeOnUKq1atgrm5ucZ6IAzcFk1Bm8Pf3x9btmxBRkYGPvvsM/Tv3x9dunRBeHg4hg4dinr16sHa2hq3b9/GkydP0KJFC2hpaTE93xsqXE8rV67Ezz//jJ9//hmDBw9G69atMX36dOjo6MDf3x8qlQrDhg0DAOjr6ytfVPDLCSIiovcbW0RERP/D5cuXsXDhQixduhR9+/ZVguUeHh6YOHEipk6ditTUVCQnJ+P27dsYNmwYzp8/D4BpboiI6OMTExOjLGro4+MDBwcH9O/fH9nZ2fD09IS3tzfGjBmDESNGAACeP3+OpUuXQqVSwcrKqphLXzKJiPJzfHw8tm/fjsjISCxduhTa2tqYOXMmYmNj8fnnn+PcuXPo0qULLCws0KZNG1y6dAna2tpQq9Vst7yhwgMTM2bMQJUqVWBmZgZ/f39MmjQJOjo6GDFiBEaPHo1JkyZh0aJFr1yDwXIiIqL3m0oKt7CIiEhDXFwcvL29sW/fPtSpUwdaWlrw8fFBXFwcli1bhqCgIGhpaWHWrFmoV68eqlSpgiFDhiAkJKS4i05ERPSPWrNmDfz9/TF58mTY2dmhadOmWL58OTZv3gwRgaenJ1JTU3HixAnMmjULjx8/xr59+/D777/j3Llz0NXV5SzndxAbG4u4uDjUrFkTEydOBACcPXsWy5Ytw08//YTx48fD09PzlfM427noDh06hCFDhiA6OhpNmzZFfHw8OnbsiPXr16N3794AgMePH2PmzJm4dOkS4uLi+MUEERFRCcKAORHR/zB37lwEBQXh4cOHyrY7d+5ArVbD1NQUly9fxpAhQ/DixQucOnUK6enpMDAwYPoVIiL6qKxZswYjR45EVFQU3N3dNfZt2bIFixYtQrly5TBixAgcOXIEu3fvhpWVFWrVqoXVq1dDR0eHgdt3cP36dQwYMAAXLlzA4MGDsXDhQmXf2bNnERwcjMuXL8Pb2xsDBw4sxpKWPLNmzUKPHj2UlHsAsHv3bsybNw/Hjx/Hli1bMGTIECXNUFZWFs6dOwcnJyc8fvwYFStWZH54IiKiEobTN4iI/gcrKys8e/YM+/fvV7ZVr14dpqamyMvLQ/369dG1a1dUqVIFmZmZMDIyUj5tJiIi+hgcOnQI3t7emDx5Mtzd3SEiEBG8fPkSAODp6Ym+ffvi/Pnz0NXVRUhICJKSkhAfH4/w8HAGy/8GtWrVwrRp02Bvb4/Y2FgcOHBA2dekSROMGjUKxsbGOHbsWDGWsuQ5ePAgrly5opFbHwAePHgAEcH+/fsxdOhQzJs3T8nJHx8fj+joaNy5cwdGRkYMlhMREZVAnGFORPQ/XL9+HY0bN0b79u2xZMkSmJuba+x/8uQJvvzyS1hbW2PJkiXFVEoiIqLic/XqVQwaNAhGRkYYN24cnJyclH2FU6w0bNgQTk5OWLlyJXJzc6GrqwsADCYWUeE6/e+6O3DgAIKCgpCbm4uJEyeibdu2yr7U1FRYWVkx5U0RFdyru3btQtWqVdGyZUs8ffoUtra2SEtLQ2RkJPr27QsgPye/u7s7KlasiA0bNvC+JiIiKqHYWiIi+h8KPhXfs2cPAgICkJycrOxLS0tD9+7d8dtvvyEwMBCA5sJbREREHwNra2usXbsWz58/x9y5c3H06FFlX0HAMDMzEzk5OahevToAKMHywsfQXyscLI+IiMDw4cPh6+uLzZs3AwDatWuHkSNHolSpUpg/fz5+/PFH5dyCtVjy8vKKpewlycSJE5U88Lq6ukhJScG4ceOwfPlynD59Gnp6eliyZAlMTEwQFRWF+Ph4bNu2DW5ubrh58ybWr1+vzCwnIiKikocBcyKiv+Dh4YEVK1Zg69atcHV1RadOndC+fXt4enoiMzMTp0+fho6ODtRqNTv9RET0UbK2tkZwcDBUKhXmzJnzSuqP69evw9TUFC1atADAAea3VRAs9/f3x6RJk6CtrY3bt28jMDAQU6ZMAQB06tQJI0eORNmyZTFu3DicPXv2T69Bfy4jIwN37tzB4cOHsWDBAgCAra0tZs+ejevXr2PZsmW4cOECunXrho0bN+LevXsYMGAAFi5cCAMDA5w9e5btQiIiohKOKVmIiN5QcnIywsPDkZqaCnNzczRp0gTe3t7Q1tZm7lUiIiLkp2fx9fWFiGDy5MlwcnLCy5cv4ebmBi0tLezatYsB23cUERGBefPmYfPmzbC3t8fmzZsxYMAAVKtWDd26dcPSpUsBADt37sSxY8ewYMEC1nkR3b9/H3PnzkVycjLatWuHqVOnAgCio6OxePFi1K1bF35+frCzswOQPyBUsWJFGBoaQqVSsV1IRERUwjFgTkT0jtRqNbS1tYu7GERERO+FgqC5lpYWAgICsGTJEly5cgXJycnQ1dXVSCtCRTd37lzk5ORg9uzZ2LVrFwYMGIBJkybh0aNHCA0NhY+PD+bMmaNxDuv8zRRu0x08eBBhYWE4e/Yshg8fjrFjxwL4T9C8fv36GDlyJBwcHDSuwbomIiIq+RgwJyIqAi5MRkRE9NeuXr2KMWPGIC4uDrVq1cLFixehq6vLmbdFFBcXh/379yMzMxPOzs7o1asX8vLykJaWhtKlS6Njx47o168f/Pz8lNnQz549w8yZM+Hn51fcxS+xxo0bh/Pnz0NLSwvJyckoV64chg4dioCAAADAli1bEBQUhMqVKyszzomIiOjDwdYqEVERMFhORET016ytrbFo0SKsXLkSS5YsgY6ODoPlRbRmzRoEBATA0dERaWlpiIiIwNOnTzFo0CBYWlri4MGDePHiBXr16gUAePnyJdq2bQt3d3f07NmzmEtfcm3duhURERGIi4tDw4YNkZmZiQkTJiA2Nhba2trw9/eHp6cnsrOzcezYMVhbWxd3kYmIiOhvxm/FiIiIiIjob1evXj0EBwczWP4WwsPDMXLkSKxevRqxsbH45ptvUK1aNWzatAlPnz4FAOjr6yM7OxvR0dH4/fffMW3aNJQvXx6enp7Q1taGWq0u5r+iZLpx4wZq1qyJxo0bo0yZMqhatSrmzJmDatWqYdmyZQgKCgIADBgwAOHh4dDS0kJeXl4xl5qIiIj+TgyYExERERHR/ysGy9/coUOHMHToUEyePBndu3cHANja2qJMmTK4f/8+nj59ivT0dDg4OMDd3R0hISGwt7fHvXv3sHr1aqhUKogI11cpooKgd9WqVaFWq3Hr1i1lu6mpKQICApCdnY3g4GBEREQAyE/VB4A5y4mIiD4wbLkSERERERG9J0xMTODo6IikpCScOXMGzZo1Q/fu3XH79m20atUK3bt3R2ZmJtzc3NCoUSO4u7sDABwdHaGtrc3Z/G/ovxfnLPjZwcEBN2/eRHBwMObMmYNy5coBAHJzc+Hk5IT27dujf//+AJiqj4iI6EPFRT+JiIiIiIjeI1evXoWvry+0tbWRkZGB7OxsREZGwsbGBikpKUhNTUVgYCBu3LiBTp06ITIyEgCgVqs5s/wNFA6WHzt2DPfu3YOpqSmsra1RsWJFxMTEwMPDA4MHD4arqyssLCzg5+cHS0tLhISEQKVSsa6JiIg+YAyYExERERERvWeuXr2KESNG4PTp0wgLC4OHhweA/wR7nz17hrS0NFhbWzNwWwQioswMnzhxInbs2IGcnByYm5vDzMwMS5YsQY0aNbBv3z74+fkhIyMDOjo6qFKlCo4fPw5dXV2NaxAREdGHhwFzIiIiIiKi99Avv/wCHx8faGlpISAgAI6OjgDwStoVznYuusDAQCxduhRbt26Fo6Mj/Pz8EBISAkdHR6xduxbm5ua4ffs2srKykJ6eDnt7e2hpaTHlDRER0UeAAXMiIiIiIqL3VEF6FgCYMmUKWrVqVcwlKpkKp2G5e/cuvLy84OPjgx49euD7779Hz5494eXlhcTERFSrVg3r1q1D9erVNa7BgQkiIqKPA5fzJiIiIiIiek9ZW1sjODgY2traGD16NC5cuFDcRSpxREQJlh88eBBGRkaYNGkSmjdvjsTERAwePBiLFi1CWFgYnJycEBcXh86dO+P+/fsa12GwnIiI6OPAgDkREREREdF7zNraGgsXLkTr1q1ha2tb3MUpUQrnG586dSpGjRqFGzduoEOHDjAzM8N3332HNm3aYMCAAQCA2rVrw8XFBZ07d0alSpWKs+hERERUTJh8jYiIiIiI6D1Xv359LF68GIBmehH63wqC5Tdu3EBKSgqCg4NRp04dZf/Dhw9x8eJF5ObmolSpUkhISED79u0xZswYAEzDQkRE9DFiwJyIiIiIiKgEYbD8rxWeWb58+XIsWbIExsbGsLS0BPCfQQdnZ2ecOXMGzZo1g56eHp4+fYotW7Yo12CwnIiI6OPDRT+JiIiIiIjog5GQkIDTp08DAIYNG4aMjAw4Ojri119/xd69e9GpUyfl2JcvX2Lnzp1ITk5GXl4eZs2aBR0dHc4sJyIi+ogxYE5EREREREQfhA0bNmDOnDlwcXGBjY0NvL29AQB//PEHmjVrhooVK2L9+vVo0KDBa6/BYDkREdHHjQFzIiIiIiIiKvE2bNgAb29vhIaGolu3btDX1wcABAYGwsnJCTY2NrCzs4OJiQnCwsJgY2MDgDnhiYiISBMD5kRERERERFSiXb58GZ6enhg5ciSGDh2qbPfw8MD27dvRtm1bzJs3D3Xq1EHjxo1hamqK5cuX41//+lcxlpqIiIjeRxxGJyIiIiIiohLtt99+w5MnT9C6dWvk5eUBAHx8fHDu3Dns2bMHKpUKU6ZMwZUrV3Du3DmcPHkSYWFhxVxqIiIieh9xhjkRERERERGVaHPnzkVQUBAePnyobLtz5w7UajVMTU1x+fJlDBkyBC9evMCpU6eQnp4OAwMD5ionIiKiV3CGOREREREREZVoVlZWePbsGfbv369sq169OkxNTZGXl4f69euja9euqFKlCjIzM2FkZARtbW2o1epiLDURERG9jxgwJyIiIiIiohLN3t4eOjo6CA0Nxc2bNzX2aWlp4cmTJ0hISEDdunVhYGCg7OMMcyIiIvpvOsVdACIiIiIiIqJ3UatWLaxevRoDBgxAmTJl4OfnBzs7OwBAWloahgwZgvv37yM2NhYAICJQqVTFWGIiIiJ6XzGHOREREREREZV4arUa69atw4gRI2BsbAxbW1u8fPkST548AQAkJCRAV1cXarWaM8uJiIjotRgwJyIiIiIiog9GcnIywsPDkZqaCnNzczRp0gTe3t7Q1tbGy5cvoaPDD62JiIjo9RgwJyIiIiIiog8eZ5YTERHRm2DAnIiIiIiIiD4ozFFOREREb0uruAtARERERERE9HdisJyIiIjeFgPmRERERERERERERERgwJyIiIiIiIiIiIiICAAD5kREREREREREREREABgwJyIiIiIiIiIiIiICwIA5EREREREREREREREABsyJiIiIiIiIiIiIiAAwYE5EREREREREREREBIABcyIiIiIiIiIiIiIiAAyYExEREREREREREREBYMCciIiIiIiIiIiIiAgAA+ZERERERERERERERACA/wO/T5FcwKMzrgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_performance_metrics(results):\n",
    "    \"\"\"Plot performance metrics for all models\"\"\"\n",
    "    # Extract metrics from test period\n",
    "    models = []\n",
    "    accuracy_rates = []\n",
    "    sortino_ratios = []\n",
    "    cvar_values = []\n",
    "    \n",
    "    for name, metrics in results.items():\n",
    "        if name != 'Buy_and_Hold':\n",
    "            models.append(name)\n",
    "            accuracy_rates.append(metrics['test_period']['Accuracy'])\n",
    "            sortino_ratios.append(metrics['test_period']['Sortino'])\n",
    "            cvar_values.append(metrics['test_period']['CVaR_95'] * 100)\n",
    "    \n",
    "    # Create subplots\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(15, 15))\n",
    "    \n",
    "    # Plot 1: Accuracy Rates\n",
    "    bars1 = ax1.bar(models, accuracy_rates, color='royalblue')\n",
    "    ax1.set_title('Model Accuracy Rates (Test Period)', pad=20)\n",
    "    ax1.set_ylabel('Accuracy (%)')\n",
    "    ax1.grid(True, linestyle='--', alpha=0.7)\n",
    "    plt.setp(ax1.xaxis.get_majorticklabels(), rotation=45, ha='right')\n",
    "    for bar in bars1:\n",
    "        height = bar.get_height()\n",
    "        ax1.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                f'{height:.1%}', ha='center', va='bottom')\n",
    "    \n",
    "    # Plot 2: Sortino Ratios\n",
    "    bars2 = ax2.bar(models, sortino_ratios, color='forestgreen')\n",
    "    ax2.set_title('Sortino Ratios (Test Period)', pad=20)\n",
    "    ax2.set_ylabel('Sortino Ratio')\n",
    "    ax2.grid(True, linestyle='--', alpha=0.7)\n",
    "    ax2.axhline(y=0, color='r', linestyle='--', alpha=0.5)\n",
    "    plt.setp(ax2.xaxis.get_majorticklabels(), rotation=45, ha='right')\n",
    "    for bar in bars2:\n",
    "        height = bar.get_height()\n",
    "        ax2.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                f'{height:.2f}', ha='center', va='bottom')\n",
    "    \n",
    "    # Plot 3: CVaR Values\n",
    "    bars3 = ax3.bar(models, cvar_values, color='darkred')\n",
    "    ax3.set_title('Conditional Value at Risk (CVaR) @ 95% (Test Period)', pad=20)\n",
    "    ax3.set_ylabel('CVaR (%)')\n",
    "    ax3.grid(True, linestyle='--', alpha=0.7)\n",
    "    plt.setp(ax3.xaxis.get_majorticklabels(), rotation=45, ha='right')\n",
    "    for bar in bars3:\n",
    "        height = bar.get_height()\n",
    "        ax3.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                f'{height:.2f}%', ha='center', va='bottom')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('performance_metrics.png', dpi=300, bbox_inches='tight')\n",
    "    plt.savefig('performance_metrics.pdf', bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "def plot_portfolio_values(results):\n",
    "    \"\"\"Plot portfolio value evolution\"\"\"\n",
    "    plt.figure(figsize=(15, 8))\n",
    "    \n",
    "    colors = ['royalblue', 'forestgreen', 'darkred', 'purple', 'orange', 'brown', 'gray']\n",
    "    \n",
    "    # Plot portfolio values for each model\n",
    "    for (name, metrics), color in zip(results.items(), colors):\n",
    "        plt.plot(metrics['test_period']['Portfolio_Values'].index, \n",
    "                metrics['test_period']['Portfolio_Values'].values,\n",
    "                label=name,\n",
    "                linewidth=2,\n",
    "                color=color)\n",
    "    \n",
    "    # Add initial investment line\n",
    "    plt.axhline(y=10000, color='black', linestyle='--', \n",
    "                label='Initial Investment ($10,000)')\n",
    "    \n",
    "    plt.title('Portfolio Value Evolution (Test Period)', pad=20)\n",
    "    plt.xlabel('Date')\n",
    "    plt.ylabel('Portfolio Value ($)')\n",
    "    plt.grid(True, linestyle='--', alpha=0.7)\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "    \n",
    "    # Format y-axis to show dollars\n",
    "    plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(\n",
    "        lambda x, p: f'${x:,.0f}'))\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('portfolio_values.png', dpi=300, bbox_inches='tight')\n",
    "    plt.savefig('portfolio_values.pdf', bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "def plot_comparative_performance(results):\n",
    "    \"\"\"Plot test vs future performance comparisons\"\"\"\n",
    "    # Extract metrics for both periods\n",
    "    models = []\n",
    "    test_returns = []\n",
    "    future_returns = []\n",
    "    test_accuracy = []\n",
    "    future_accuracy = []\n",
    "    \n",
    "    for name, metrics in results.items():\n",
    "        if name != 'Buy_and_Hold':  # Skip Buy_and_Hold for accuracy comparison\n",
    "            models.append(name)\n",
    "            test_returns.append(metrics['test_period']['Total_Return'])\n",
    "            future_returns.append(metrics['future_period']['Total_Return'])\n",
    "            test_accuracy.append(metrics['test_period']['Accuracy'])\n",
    "            future_accuracy.append(metrics['future_period']['Accuracy'])\n",
    "    \n",
    "    # Create figure with 2 subplots\n",
    "    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 12))\n",
    "    \n",
    "    # Plot 1: Returns Comparison\n",
    "    x = np.arange(len(models))\n",
    "    width = 0.35\n",
    "    \n",
    "    bars1 = ax1.bar(x - width/2, test_returns, width, label='Test Period', color='skyblue')\n",
    "    bars2 = ax1.bar(x + width/2, future_returns, width, label='Future Period', color='lightcoral')\n",
    "    \n",
    "    ax1.set_title('Total Returns: Test vs Future Period', pad=20)\n",
    "    ax1.set_ylabel('Return (%)')\n",
    "    ax1.set_xticks(x)\n",
    "    ax1.set_xticklabels(models, rotation=45, ha='right')\n",
    "    ax1.legend()\n",
    "    ax1.grid(True, linestyle='--', alpha=0.7)\n",
    "    \n",
    "    # Add value labels\n",
    "    def autolabel(bars):\n",
    "        for bar in bars:\n",
    "            height = bar.get_height()\n",
    "            ax1.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                    f'{height:.1f}%', ha='center', va='bottom')\n",
    "    \n",
    "    autolabel(bars1)\n",
    "    autolabel(bars2)\n",
    "    \n",
    "    # Plot 2: Accuracy Comparison\n",
    "    bars3 = ax2.bar(x - width/2, [acc*100 for acc in test_accuracy], width, \n",
    "                    label='Test Period', color='skyblue')\n",
    "    bars4 = ax2.bar(x + width/2, [acc*100 for acc in future_accuracy], width, \n",
    "                    label='Future Period', color='lightcoral')\n",
    "    \n",
    "    ax2.set_title('Accuracy Rates: Test vs Future Period', pad=20)\n",
    "    ax2.set_ylabel('Accuracy (%)')\n",
    "    ax2.set_xticks(x)\n",
    "    ax2.set_xticklabels(models, rotation=45, ha='right')\n",
    "    ax2.legend()\n",
    "    ax2.grid(True, linestyle='--', alpha=0.7)\n",
    "    \n",
    "    # Add value labels\n",
    "    def autolabel_accuracy(bars):\n",
    "        for bar in bars:\n",
    "            height = bar.get_height()\n",
    "            ax2.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                    f'{height:.1f}%', ha='center', va='bottom')\n",
    "    \n",
    "    autolabel_accuracy(bars3)\n",
    "    autolabel_accuracy(bars4)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('comparative_performance.png', dpi=300, bbox_inches='tight')\n",
    "    plt.savefig('comparative_performance.pdf', bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "def plot_performance_stability(results):\n",
    "    \"\"\"Plot stability metrics comparing test and future periods\"\"\"\n",
    "    # Extract Sortino and CVaR for both periods\n",
    "    models = []\n",
    "    test_sortino = []\n",
    "    future_sortino = []\n",
    "    test_cvar = []\n",
    "    future_cvar = []\n",
    "    \n",
    "    for name, metrics in results.items():\n",
    "        models.append(name)\n",
    "        test_sortino.append(metrics['test_period']['Sortino'])\n",
    "        future_sortino.append(metrics['future_period']['Sortino'])\n",
    "        test_cvar.append(metrics['test_period']['CVaR_95'] * 100)\n",
    "        future_cvar.append(metrics['future_period']['CVaR_95'] * 100)\n",
    "    \n",
    "    # Create figure with 2 subplots\n",
    "    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 12))\n",
    "    \n",
    "    # Plot 1: Sortino Ratio Comparison\n",
    "    x = np.arange(len(models))\n",
    "    width = 0.35\n",
    "    \n",
    "    bars1 = ax1.bar(x - width/2, test_sortino, width, label='Test Period', color='skyblue')\n",
    "    bars2 = ax1.bar(x + width/2, future_sortino, width, label='Future Period', color='lightcoral')\n",
    "    \n",
    "    ax1.set_title('Sortino Ratio Stability', pad=20)\n",
    "    ax1.set_ylabel('Sortino Ratio')\n",
    "    ax1.set_xticks(x)\n",
    "    ax1.set_xticklabels(models, rotation=45, ha='right')\n",
    "    ax1.legend()\n",
    "    ax1.grid(True, linestyle='--', alpha=0.7)\n",
    "    ax1.axhline(y=0, color='black', linestyle='--', alpha=0.5)\n",
    "    \n",
    "    # Add value labels\n",
    "    def autolabel(bars):\n",
    "        for bar in bars:\n",
    "            height = bar.get_height()\n",
    "            ax1.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                    f'{height:.2f}', ha='center', va='bottom')\n",
    "    \n",
    "    autolabel(bars1)\n",
    "    autolabel(bars2)\n",
    "    \n",
    "    # Plot 2: CVaR Comparison\n",
    "    bars3 = ax2.bar(x - width/2, test_cvar, width, label='Test Period', color='skyblue')\n",
    "    bars4 = ax2.bar(x + width/2, future_cvar, width, label='Future Period', color='lightcoral')\n",
    "    \n",
    "    ax2.set_title('CVaR@95% Stability', pad=20)\n",
    "    ax2.set_ylabel('CVaR (%)')\n",
    "    ax2.set_xticks(x)\n",
    "    ax2.set_xticklabels(models, rotation=45, ha='right')\n",
    "    ax2.legend()\n",
    "    ax2.grid(True, linestyle='--', alpha=0.7)\n",
    "    \n",
    "    def autolabel_cvar(bars):\n",
    "        for bar in bars:\n",
    "            height = bar.get_height()\n",
    "            ax2.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                    f'{height:.2f}%', ha='center', va='bottom')\n",
    "    \n",
    "    autolabel_cvar(bars3)\n",
    "    autolabel_cvar(bars4)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('performance_stability.png', dpi=300, bbox_inches='tight')\n",
    "    plt.savefig('performance_stability.pdf', bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "# Now use the results from your previous execution\n",
    "plot_performance_metrics(results)\n",
    "plot_portfolio_values(results)\n",
    "plot_comparative_performance(results)\n",
    "plot_performance_stability(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ea1e74e-d522-4c01-a62e-0543b8eeb08a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x2500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_focused_portfolio_evolution(results_all):\n",
    "    \"\"\"Plot portfolio values across periods focusing on top performers\"\"\"\n",
    "    periods = [\n",
    "        ('2018', '2018-01-01', '2018-12-31'),\n",
    "        ('2019', '2019-01-01', '2019-12-31'),\n",
    "        ('2020', '2020-01-01', '2020-12-31'),\n",
    "        ('2021', '2021-01-01', '2021-12-31'),\n",
    "        ('2022', '2022-01-01', '2022-12-31'),\n",
    "        ('2023', '2023-01-01', '2023-12-31'),\n",
    "        ('2018-19', '2018-01-01', '2019-12-31'),\n",
    "        ('2020-21', '2020-01-01', '2021-12-31'),\n",
    "        ('2022-23', '2022-01-01', '2023-12-31'),\n",
    "        ('2018-23', '2018-01-01', '2023-12-31')\n",
    "    ]\n",
    "    \n",
    "    # Create a 5x2 grid of subplots\n",
    "    fig, axs = plt.subplots(5, 2, figsize=(20, 25))\n",
    "    axs = axs.ravel()\n",
    "    \n",
    "    # Focus colors on key models\n",
    "    colors = {\n",
    "        'Neural_Base': 'royalblue',      # Strong emphasis\n",
    "        'Logistic_Base': 'forestgreen',  # Strong emphasis\n",
    "        'Buy_and_Hold': 'gray'           # Benchmark\n",
    "    }\n",
    "    \n",
    "    # Other models in light colors\n",
    "    other_colors = {model: 'lightgray' for model in [\n",
    "        'RandomForest_Base', 'GradientBoosting_Base', 'Neural_Enhanced',\n",
    "        'XGBoost_Enhanced', 'DecisionTree_Base', 'AdaBoost_Base',\n",
    "        'XGBoost_Base', 'Logistic_Enhanced', 'DecisionTree_Enhanced',\n",
    "        'RandomForest_Enhanced', 'GradientBoosting_Enhanced', 'AdaBoost_Enhanced'\n",
    "    ]}\n",
    "    \n",
    "    colors.update(other_colors)\n",
    "    \n",
    "    # Plot each period\n",
    "    for idx, (period_name, start_date, end_date) in enumerate(periods):\n",
    "        key = f\"EURUSD_{start_date}_{end_date}\"\n",
    "        \n",
    "        # First plot other models in light gray\n",
    "        for model_name, metrics in results_all[key].items():\n",
    "            if model_name not in ['Neural_Base', 'Logistic_Base', 'Buy_and_Hold']:\n",
    "                # Plot with reduced alpha for background models\n",
    "                axs[idx].plot(metrics['test_period']['Portfolio_Values'].index,\n",
    "                            metrics['test_period']['Portfolio_Values'].values,\n",
    "                            color=colors[model_name],\n",
    "                            alpha=0.2,\n",
    "                            linewidth=1)\n",
    "        \n",
    "        # Then plot key models on top\n",
    "        for model_name, metrics in results_all[key].items():\n",
    "            if model_name in ['Neural_Base', 'Logistic_Base', 'Buy_and_Hold']:\n",
    "                axs[idx].plot(metrics['test_period']['Portfolio_Values'].index,\n",
    "                            metrics['test_period']['Portfolio_Values'].values,\n",
    "                            label=model_name,\n",
    "                            color=colors[model_name],\n",
    "                            linewidth=2.5)\n",
    "                \n",
    "                axs[idx].plot(metrics['future_period']['Portfolio_Values'].index,\n",
    "                            metrics['future_period']['Portfolio_Values'].values,\n",
    "                            label=f\"{model_name} (Future)\",\n",
    "                            color=colors[model_name],\n",
    "                            linestyle='--',\n",
    "                            linewidth=2.5)\n",
    "        \n",
    "        # Add reference line for initial investment\n",
    "        axs[idx].axhline(y=10000, color='black', linestyle=':', \n",
    "                        label='Initial Investment ($10,000)')\n",
    "        \n",
    "        # Customize each subplot\n",
    "        axs[idx].set_title(f'Period: {period_name}', fontsize=12, fontweight='bold')\n",
    "        axs[idx].grid(True, linestyle='--', alpha=0.7)\n",
    "        axs[idx].set_ylabel('Portfolio Value ($)')\n",
    "        axs[idx].yaxis.set_major_formatter(plt.FuncFormatter(\n",
    "            lambda x, p: f'${x:,.0f}'))\n",
    "        \n",
    "        # Rotate x-axis dates\n",
    "        plt.setp(axs[idx].xaxis.get_majorticklabels(), rotation=45)\n",
    "        \n",
    "        # Add legend to first subplot only\n",
    "        if idx == 0:\n",
    "            axs[idx].legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "    \n",
    "    # Add overall title\n",
    "    fig.suptitle('Portfolio Evolution Across Different Time Periods\\n' +\n",
    "                'Highlighting Top Performers: Neural Base and Logistic Base',\n",
    "                fontsize=16, y=1.02)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('focused_portfolio_evolution.png', dpi=300, bbox_inches='tight')\n",
    "    plt.savefig('focused_portfolio_evolution.pdf', bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "# Run the visualization\n",
    "plot_focused_portfolio_evolution(results_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbf01d81-4a35-43a3-9abf-fc4eea363297",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ba6aeb1d-7dfd-4c2c-8ba3-ede8250e75a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (1564, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (269, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (1564, 9)\n",
      "Enhanced features shape before cleaning: (1564, 9)\n",
      "Features shape after cleaning: (1563, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 6.34%\n",
      "Sortino: 0.04\n",
      "Max Drawdown: -8.41%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -6.56%\n",
      "Sortino: -1.63\n",
      "Max Drawdown: -8.25%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8082\n",
      "Total Return: 269.76%\n",
      "Sortino: 21.30\n",
      "Max Drawdown: -0.91%\n",
      "Number of Trades: 188\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8015\n",
      "Total Return: 74.93%\n",
      "Sortino: 18.14\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 139\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5934\n",
      "Total Return: 19.56%\n",
      "Sortino: 0.87\n",
      "Max Drawdown: -15.24%\n",
      "Number of Trades: 153\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6105\n",
      "Total Return: 25.45%\n",
      "Sortino: 3.41\n",
      "Max Drawdown: -2.37%\n",
      "Number of Trades: 106\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5652\n",
      "Total Return: 23.08%\n",
      "Sortino: 1.17\n",
      "Max Drawdown: -8.41%\n",
      "Number of Trades: 115\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6105\n",
      "Total Return: 21.04%\n",
      "Sortino: 2.59\n",
      "Max Drawdown: -2.78%\n",
      "Number of Trades: 90\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6215\n",
      "Total Return: 52.38%\n",
      "Sortino: 2.90\n",
      "Max Drawdown: -4.33%\n",
      "Number of Trades: 131\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6142\n",
      "Total Return: 29.27%\n",
      "Sortino: 4.36\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 120\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7775\n",
      "Total Return: 237.29%\n",
      "Sortino: 16.09\n",
      "Max Drawdown: -1.46%\n",
      "Number of Trades: 206\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7678\n",
      "Total Return: 69.40%\n",
      "Sortino: 12.42\n",
      "Max Drawdown: -1.38%\n",
      "Number of Trades: 131\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5882\n",
      "Total Return: 33.17%\n",
      "Sortino: 1.72\n",
      "Max Drawdown: -6.14%\n",
      "Number of Trades: 128\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6105\n",
      "Total Return: 31.45%\n",
      "Sortino: 4.90\n",
      "Max Drawdown: -1.58%\n",
      "Number of Trades: 102\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6240\n",
      "Total Return: 59.03%\n",
      "Sortino: 3.15\n",
      "Max Drawdown: -7.83%\n",
      "Number of Trades: 129\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6142\n",
      "Total Return: 27.43%\n",
      "Sortino: 3.84\n",
      "Max Drawdown: -2.51%\n",
      "Number of Trades: 102\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7340\n",
      "Total Return: 176.52%\n",
      "Sortino: 9.87\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 150\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6704\n",
      "Total Return: 48.85%\n",
      "Sortino: 8.13\n",
      "Max Drawdown: -1.59%\n",
      "Number of Trades: 77\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5575\n",
      "Total Return: 19.41%\n",
      "Sortino: 0.92\n",
      "Max Drawdown: -8.41%\n",
      "Number of Trades: 80\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5918\n",
      "Total Return: 11.68%\n",
      "Sortino: 1.16\n",
      "Max Drawdown: -3.24%\n",
      "Number of Trades: 44\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5269\n",
      "Total Return: 1.52%\n",
      "Sortino: -0.30\n",
      "Max Drawdown: -8.41%\n",
      "Number of Trades: 117\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5581\n",
      "Total Return: 9.28%\n",
      "Sortino: 0.80\n",
      "Max Drawdown: -5.07%\n",
      "Number of Trades: 100\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6113\n",
      "Total Return: 44.31%\n",
      "Sortino: 2.35\n",
      "Max Drawdown: -6.14%\n",
      "Number of Trades: 143\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6217\n",
      "Total Return: 27.18%\n",
      "Sortino: 3.79\n",
      "Max Drawdown: -2.16%\n",
      "Number of Trades: 115\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7775\n",
      "Total Return: 240.80%\n",
      "Sortino: 18.14\n",
      "Max Drawdown: -1.79%\n",
      "Number of Trades: 212\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7715\n",
      "Total Return: 71.33%\n",
      "Sortino: 16.78\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 133\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5243\n",
      "Total Return: 5.65%\n",
      "Sortino: -0.00\n",
      "Max Drawdown: -8.41%\n",
      "Number of Trades: 69\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4944\n",
      "Total Return: 1.06%\n",
      "Sortino: -0.46\n",
      "Max Drawdown: -7.21%\n",
      "Number of Trades: 84\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5422\n",
      "Total Return: 1.30%\n",
      "Sortino: -0.29\n",
      "Max Drawdown: -7.74%\n",
      "Number of Trades: 144\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5131\n",
      "Total Return: -4.32%\n",
      "Sortino: -1.22\n",
      "Max Drawdown: -7.65%\n",
      "Number of Trades: 89\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'plot_model_performance' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[14], line 9\u001b[0m\n\u001b[0;32m      2\u001b[0m results \u001b[38;5;241m=\u001b[39m main_enhanced_flexible(\n\u001b[0;32m      3\u001b[0m     currency_pair\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEURUSD=X\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      4\u001b[0m     train_start\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2018-01-01\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      5\u001b[0m     train_end\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2023-12-31\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m      6\u001b[0m )\n\u001b[0;32m      8\u001b[0m \u001b[38;5;66;03m# Then create the visualization\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m plot_model_performance(results)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plot_model_performance' is not defined"
     ]
    }
   ],
   "source": [
    "# Get results first\n",
    "results = main_enhanced_flexible(\n",
    "    currency_pair='EURUSD=X',\n",
    "    train_start='2018-01-01',\n",
    "    train_end='2023-12-31'\n",
    ")\n",
    "\n",
    "# Then create the visualization\n",
    "plot_model_performance(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5f00ff8a-35dd-4541-bc08-48c71f5523c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model_performance(results):\n",
    "    \"\"\"Plot model performance including buy & hold benchmark for both periods\"\"\"\n",
    "    plt.figure(figsize=(15, 10))\n",
    "    \n",
    "    # Create two subplots\n",
    "    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 12))\n",
    "    \n",
    "    # Plot Test Period (Upper subplot)\n",
    "    # First plot buy and hold benchmark\n",
    "    ax1.plot(results['Buy_and_Hold']['test_period']['Portfolio_Values'],\n",
    "             label='Buy and Hold',\n",
    "             color='black',\n",
    "             linewidth=2)\n",
    "    \n",
    "    # Plot baseline models\n",
    "    for name, result in results.items():\n",
    "        if 'Base' in name and name != 'Buy_and_Hold':\n",
    "            ax1.plot(result['test_period']['Portfolio_Values'], \n",
    "                    label=name,\n",
    "                    linestyle='--',\n",
    "                    alpha=0.7)\n",
    "    \n",
    "    # Plot enhanced models\n",
    "    for name, result in results.items():\n",
    "        if 'Enhanced' in name:\n",
    "            ax1.plot(result['test_period']['Portfolio_Values'], \n",
    "                    label=name,\n",
    "                    alpha=0.7)\n",
    "    \n",
    "    ax1.set_title('Test Period Performance')\n",
    "    ax1.set_xlabel('Date')\n",
    "    ax1.set_ylabel('Portfolio Value ($)')\n",
    "    ax1.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "    ax1.grid(True)\n",
    "    \n",
    "    # Plot Future Period (Lower subplot)\n",
    "    # First plot buy and hold benchmark\n",
    "    ax2.plot(results['Buy_and_Hold']['future_period']['Portfolio_Values'],\n",
    "             label='Buy and Hold',\n",
    "             color='black',\n",
    "             linewidth=2)\n",
    "    \n",
    "    # Plot baseline models\n",
    "    for name, result in results.items():\n",
    "        if 'Base' in name and name != 'Buy_and_Hold':\n",
    "            ax2.plot(result['future_period']['Portfolio_Values'], \n",
    "                    label=name,\n",
    "                    linestyle='--',\n",
    "                    alpha=0.7)\n",
    "    \n",
    "    # Plot enhanced models\n",
    "    for name, result in results.items():\n",
    "        if 'Enhanced' in name:\n",
    "            ax2.plot(result['future_period']['Portfolio_Values'], \n",
    "                    label=name,\n",
    "                    alpha=0.7)\n",
    "    \n",
    "    ax2.set_title('Future Period Performance')\n",
    "    ax2.set_xlabel('Date')\n",
    "    ax2.set_ylabel('Portfolio Value ($)')\n",
    "    ax2.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "    ax2.grid(True)\n",
    "    \n",
    "    plt.suptitle('Model Performance Comparison\\n(Including 5 pip transaction costs per round trip)', \n",
    "                 fontsize=14, y=1.02)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b75a837a-12ba-4b40-b2eb-afe84c26b099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the visualization using your existing results\n",
    "plot_model_performance(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6347cdeb-527b-40c0-9456-dd7a81755e1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "99b26dc1-b35f-4428-96ad-0c56485cf2c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_accuracy_comparison(results):\n",
    "    \"\"\"Compare accuracy between test and future periods\"\"\"\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    \n",
    "    names = []\n",
    "    test_accuracies = []\n",
    "    future_accuracies = []\n",
    "    \n",
    "    for name, result in results.items():\n",
    "        if name != 'Buy_and_Hold':\n",
    "            names.append(name)\n",
    "            test_accuracies.append(result['test_period']['Accuracy'] * 100)\n",
    "            future_accuracies.append(result['future_period']['Accuracy'] * 100)\n",
    "    \n",
    "    x = np.arange(len(names))\n",
    "    width = 0.35\n",
    "    \n",
    "    plt.bar(x - width/2, test_accuracies, width, label='Test Period', color='blue', alpha=0.6)\n",
    "    plt.bar(x + width/2, future_accuracies, width, label='Future Period', color='red', alpha=0.6)\n",
    "    \n",
    "    plt.title('Accuracy Comparison: Test vs Future Period')\n",
    "    plt.xlabel('Models')\n",
    "    plt.ylabel('Accuracy (%)')\n",
    "    plt.xticks(x, names, rotation=45, ha='right')\n",
    "    plt.legend()\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    # Add value labels\n",
    "    for i, v in enumerate(test_accuracies):\n",
    "        plt.text(i - width/2, v, f'{v:.1f}%', ha='center', va='bottom')\n",
    "    for i, v in enumerate(future_accuracies):\n",
    "        plt.text(i + width/2, v, f'{v:.1f}%', ha='center', va='bottom')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('accuracy_comparison.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "def plot_average_gains(results):\n",
    "    \"\"\"Compare average gains per correct prediction\"\"\"\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    \n",
    "    names = []\n",
    "    test_gains = []\n",
    "    future_gains = []\n",
    "    \n",
    "    for name, result in results.items():\n",
    "        if name != 'Buy_and_Hold':\n",
    "            names.append(name)\n",
    "            test_returns = result['test_period']['Portfolio_Values'].pct_change().dropna()\n",
    "            future_returns = result['future_period']['Portfolio_Values'].pct_change().dropna()\n",
    "            \n",
    "            test_gains.append(test_returns[test_returns > 0].mean() * 100)\n",
    "            future_gains.append(future_returns[future_returns > 0].mean() * 100)\n",
    "    \n",
    "    x = np.arange(len(names))\n",
    "    width = 0.35\n",
    "    \n",
    "    plt.bar(x - width/2, test_gains, width, label='Test Period', color='green', alpha=0.6)\n",
    "    plt.bar(x + width/2, future_gains, width, label='Future Period', color='lightgreen', alpha=0.6)\n",
    "    \n",
    "    plt.title('Average Gain per Correct Prediction')\n",
    "    plt.xlabel('Models')\n",
    "    plt.ylabel('Average Gain (%)')\n",
    "    plt.xticks(x, names, rotation=45, ha='right')\n",
    "    plt.legend()\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    # Add value labels\n",
    "    for i, v in enumerate(test_gains):\n",
    "        plt.text(i - width/2, v, f'{v:.2f}%', ha='center', va='bottom')\n",
    "    for i, v in enumerate(future_gains):\n",
    "        plt.text(i + width/2, v, f'{v:.2f}%', ha='center', va='bottom')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('average_gains_comparison.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "def plot_average_losses(results):\n",
    "    \"\"\"Compare average losses per incorrect prediction\"\"\"\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    \n",
    "    names = []\n",
    "    test_losses = []\n",
    "    future_losses = []\n",
    "    \n",
    "    for name, result in results.items():\n",
    "        if name != 'Buy_and_Hold':\n",
    "            names.append(name)\n",
    "            test_returns = result['test_period']['Portfolio_Values'].pct_change().dropna()\n",
    "            future_returns = result['future_period']['Portfolio_Values'].pct_change().dropna()\n",
    "            \n",
    "            test_losses.append(abs(test_returns[test_returns < 0].mean() * 100))\n",
    "            future_losses.append(abs(future_returns[future_returns < 0].mean() * 100))\n",
    "    \n",
    "    x = np.arange(len(names))\n",
    "    width = 0.35\n",
    "    \n",
    "    plt.bar(x - width/2, test_losses, width, label='Test Period', color='red', alpha=0.6)\n",
    "    plt.bar(x + width/2, future_losses, width, label='Future Period', color='lightcoral', alpha=0.6)\n",
    "    \n",
    "    plt.title('Average Loss per Incorrect Prediction')\n",
    "    plt.xlabel('Models')\n",
    "    plt.ylabel('Average Loss (%)')\n",
    "    plt.xticks(x, names, rotation=45, ha='right')\n",
    "    plt.legend()\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    # Add value labels\n",
    "    for i, v in enumerate(test_losses):\n",
    "        plt.text(i - width/2, v, f'{v:.2f}%', ha='center', va='bottom')\n",
    "    for i, v in enumerate(future_losses):\n",
    "        plt.text(i + width/2, v, f'{v:.2f}%', ha='center', va='bottom')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('average_losses_comparison.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "def plot_total_returns(results):\n",
    "    \"\"\"Compare total returns\"\"\"\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    \n",
    "    names = []\n",
    "    test_returns = []\n",
    "    future_returns = []\n",
    "    \n",
    "    for name, result in results.items():\n",
    "        names.append(name)\n",
    "        test_returns.append(result['test_period']['Total_Return'])\n",
    "        future_returns.append(result['future_period']['Total_Return'])\n",
    "    \n",
    "    x = np.arange(len(names))\n",
    "    width = 0.35\n",
    "    \n",
    "    plt.bar(x - width/2, test_returns, width, label='Test Period', color='purple', alpha=0.6)\n",
    "    plt.bar(x + width/2, future_returns, width, label='Future Period', color='plum', alpha=0.6)\n",
    "    \n",
    "    plt.title('Total Returns Comparison')\n",
    "    plt.xlabel('Models')\n",
    "    plt.ylabel('Total Return (%)')\n",
    "    plt.xticks(x, names, rotation=45, ha='right')\n",
    "    plt.legend()\n",
    "    plt.grid(True, alpha=0.3)\n",
    "    \n",
    "    # Add value labels\n",
    "    for i, v in enumerate(test_returns):\n",
    "        plt.text(i - width/2, v, f'{v:.1f}%', ha='center', va='bottom')\n",
    "    for i, v in enumerate(future_returns):\n",
    "        plt.text(i + width/2, v, f'{v:.1f}%', ha='center', va='bottom')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('total_returns_comparison.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3c1fffac-5768-4cfa-ac38-4b237a994aff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run each plot individually\n",
    "plot_accuracy_comparison(results)\n",
    "plot_average_gains(results)\n",
    "plot_average_losses(results)\n",
    "plot_total_returns(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a19fb679-151f-4ccf-8cae-3c0c71065caf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2e3d06e-8e20-469a-981d-8791cf0aa4ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_average_performance(results_all):\n",
    "    \"\"\"Calculate average returns across all periods and currency pairs\"\"\"\n",
    "    # Initialize storage for all returns\n",
    "    model_returns = {}\n",
    "    \n",
    "    # Collect returns for each model across all periods\n",
    "    for period_results in results_all.values():  # This loops through all periods\n",
    "        for model_name, model_data in period_results.items():\n",
    "            if model_name not in model_returns:\n",
    "                model_returns[model_name] = {\n",
    "                    'test_returns': [],\n",
    "                    'future_returns': []\n",
    "                }\n",
    "            \n",
    "            # Store returns for both periods\n",
    "            model_returns[model_name]['test_returns'].append(\n",
    "                model_data['test_period']['Total_Return'])\n",
    "            model_returns[model_name]['future_returns'].append(\n",
    "                model_data['future_period']['Total_Return'])\n",
    "    \n",
    "    # Calculate averages\n",
    "    averages = {}\n",
    "    for model_name, returns in model_returns.items():\n",
    "        averages[model_name] = {\n",
    "            'avg_test_return': np.mean(returns['test_returns']),\n",
    "            'avg_future_return': np.mean(returns['future_returns']),\n",
    "            'std_test_return': np.std(returns['test_returns']),\n",
    "            'std_future_return': np.std(returns['future_returns'])\n",
    "        }\n",
    "    \n",
    "    return averages\n",
    "\n",
    "# Use it like this:\n",
    "avg_performance = calculate_average_performance(results_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f30aa0c0-fb06-412f-93cd-b8d3442f231e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Ensure plots are displayed\n",
    "%matplotlib inline\n",
    "\n",
    "# Then run the visualization functions\n",
    "plot_accuracy_comparison(results)\n",
    "plot_average_gains(results)\n",
    "plot_average_losses(results)\n",
    "plot_total_returns(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5914df76-e3ef-4c7c-b649-4c8558507d37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "68cf5e0c-7329-4590-a0e7-cb450d5ef4bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def multi_period_analysis():\n",
    "    # Define all periods to analyze\n",
    "    periods = [\n",
    "        ('2018-01-01', '2018-12-31', '2018'),\n",
    "        ('2019-01-01', '2019-12-31', '2019'),\n",
    "        ('2020-01-01', '2020-12-31', '2020'),\n",
    "        ('2021-01-01', '2021-12-31', '2021'),\n",
    "        ('2022-01-01', '2022-12-31', '2022'),\n",
    "        ('2023-01-01', '2023-12-31', '2023'),\n",
    "        ('2018-01-01', '2019-12-31', '2018-19'),\n",
    "        ('2020-01-01', '2021-12-31', '2020-21'),\n",
    "        ('2022-01-01', '2023-12-31', '2022-23'),\n",
    "        ('2018-01-01', '2023-12-31', '2018-23')\n",
    "    ]\n",
    "    \n",
    "    # Define currency pairs\n",
    "    currencies = [\n",
    "        'EURUSD=X',\n",
    "        'AUDUSD=X',\n",
    "        'MXNUSD=X',\n",
    "        'JPYUSD=X',\n",
    "        'CNYUSD=X',\n",
    "        'CHFUSD=X'\n",
    "    ]\n",
    "    \n",
    "    # Store all results\n",
    "    all_results = {}\n",
    "    \n",
    "    # Run analysis for each combination\n",
    "    for currency in currencies:\n",
    "        for start_date, end_date, period_name in periods:\n",
    "            key = f\"{currency}_{period_name}\"\n",
    "            print(f\"\\nAnalyzing {key}\")\n",
    "            \n",
    "            try:\n",
    "                results = main_enhanced_flexible(\n",
    "                    currency_pair=currency,\n",
    "                    train_start=start_date,\n",
    "                    train_end=end_date\n",
    "                )\n",
    "                all_results[key] = results\n",
    "            except Exception as e:\n",
    "                print(f\"Error analyzing {key}: {str(e)}\")\n",
    "    \n",
    "    return all_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "163dbd7d-3883-4b30-b0eb-ef59874d1872",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1d56320e-31c5-4ddd-98c7-4e8269da347f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_aggregate_metrics(all_results):\n",
    "    # Initialize storage for aggregated metrics\n",
    "    aggregate_metrics = {\n",
    "        'Logistic_Base': {'test_period': {}, 'future_period': {}},\n",
    "        'Neural_Base': {'test_period': {}, 'future_period': {}},\n",
    "        'Other_ML_Base': {'test_period': {}, 'future_period': {}},\n",
    "        'Enhanced_Models': {'test_period': {}, 'future_period': {}},\n",
    "        'Buy_and_Hold': {'test_period': {}, 'future_period': {}}\n",
    "    }\n",
    "    \n",
    "    # Group model names\n",
    "    model_groups = {\n",
    "        'Other_ML_Base': ['DecisionTree_Base', 'RandomForest_Base', 'GradientBoosting_Base', \n",
    "                         'AdaBoost_Base', 'XGBoost_Base'],\n",
    "        'Enhanced_Models': ['Logistic_Enhanced', 'Neural_Enhanced', 'DecisionTree_Enhanced',\n",
    "                          'RandomForest_Enhanced', 'GradientBoosting_Enhanced',\n",
    "                          'AdaBoost_Enhanced', 'XGBoost_Enhanced']\n",
    "    }\n",
    "    \n",
    "    # Collect and average metrics\n",
    "    for results in all_results.values():\n",
    "        for model_name, model_results in results.items():\n",
    "            for period in ['test_period', 'future_period']:\n",
    "                # Handle individual models\n",
    "                if model_name == 'Logistic_Base':\n",
    "                    target_group = 'Logistic_Base'\n",
    "                elif model_name == 'Neural_Base':\n",
    "                    target_group = 'Neural_Base'\n",
    "                elif model_name == 'Buy_and_Hold':\n",
    "                    target_group = 'Buy_and_Hold'\n",
    "                # Handle grouped models\n",
    "                elif model_name in model_groups['Other_ML_Base']:\n",
    "                    target_group = 'Other_ML_Base'\n",
    "                elif model_name in model_groups['Enhanced_Models']:\n",
    "                    target_group = 'Enhanced_Models'\n",
    "                else:\n",
    "                    continue\n",
    "                \n",
    "                # Initialize lists if needed\n",
    "                if not aggregate_metrics[target_group][period]:\n",
    "                    aggregate_metrics[target_group][period] = {\n",
    "                        'accuracy': [],\n",
    "                        'total_return': [],\n",
    "                        'sortino': [],\n",
    "                        'cvar': []\n",
    "                    }\n",
    "                \n",
    "                # Collect metrics\n",
    "                if model_results[period].get('Accuracy') is not None:\n",
    "                    aggregate_metrics[target_group][period]['accuracy'].append(\n",
    "                        model_results[period]['Accuracy'])\n",
    "                aggregate_metrics[target_group][period]['total_return'].append(\n",
    "                    model_results[period]['Total_Return'])\n",
    "                aggregate_metrics[target_group][period]['sortino'].append(\n",
    "                    model_results[period]['Sortino'])\n",
    "                aggregate_metrics[target_group][period]['cvar'].append(\n",
    "                    model_results[period]['CVaR_95'])\n",
    "    \n",
    "    # Calculate averages\n",
    "    average_metrics = {}\n",
    "    for group_name, metrics in aggregate_metrics.items():\n",
    "        average_metrics[group_name] = {\n",
    "            'test_period': {\n",
    "                'Accuracy': np.mean(metrics['test_period']['accuracy']),\n",
    "                'Total_Return': np.mean(metrics['test_period']['total_return']),\n",
    "                'Sortino': np.mean(metrics['test_period']['sortino']),\n",
    "                'CVaR_95': np.mean(metrics['test_period']['cvar'])\n",
    "            },\n",
    "            'future_period': {\n",
    "                'Accuracy': np.mean(metrics['future_period']['accuracy']),\n",
    "                'Total_Return': np.mean(metrics['future_period']['total_return']),\n",
    "                'Sortino': np.mean(metrics['future_period']['sortino']),\n",
    "                'CVaR_95': np.mean(metrics['future_period']['cvar'])\n",
    "            }\n",
    "        }\n",
    "    \n",
    "    return average_metrics\n",
    "\n",
    "def plot_aggregate_comparisons(average_metrics):\n",
    "    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 15))\n",
    "    \n",
    "    models = ['Logistic_Base', 'Neural_Base', 'Other_ML_Base', 'Enhanced_Models', 'Buy_and_Hold']\n",
    "    x = np.arange(len(models))\n",
    "    width = 0.35\n",
    "    \n",
    "    # Color scheme\n",
    "    colors = {\n",
    "        'test': ['royalblue', 'royalblue', 'gray', 'forestgreen', 'red'],\n",
    "        'future': ['lightblue', 'lightblue', 'lightgray', 'lightgreen', 'lightcoral']\n",
    "    }\n",
    "    \n",
    "    # Plot metrics\n",
    "    for ax, (metric, ylabel, is_percentage) in zip(\n",
    "        [ax1, ax2, ax3, ax4],\n",
    "        [('Accuracy', 'Accuracy (%)', True),\n",
    "         ('Total_Return', 'Total Return (%)', False),\n",
    "         ('Sortino', 'Sortino Ratio', False),\n",
    "         ('CVaR_95', 'CVaR (%)', True)]\n",
    "    ):\n",
    "        test_values = []\n",
    "        future_values = []\n",
    "        \n",
    "        for model in models:\n",
    "            value_test = average_metrics[model]['test_period'][metric]\n",
    "            value_future = average_metrics[model]['future_period'][metric]\n",
    "            \n",
    "            if is_percentage:\n",
    "                value_test *= 100\n",
    "                value_future *= 100\n",
    "                \n",
    "            test_values.append(value_test)\n",
    "            future_values.append(value_future)\n",
    "        \n",
    "        # Create bars\n",
    "        bars1 = ax.bar(x - width/2, test_values, width, label='Test Period')\n",
    "        bars2 = ax.bar(x + width/2, future_values, width, label='Future Period')\n",
    "        \n",
    "        # Color bars\n",
    "        for idx, (bar1, bar2) in enumerate(zip(bars1, bars2)):\n",
    "            bar1.set_color(colors['test'][idx])\n",
    "            bar2.set_color(colors['future'][idx])\n",
    "        \n",
    "        # Add value labels\n",
    "        for bars in [bars1, bars2]:\n",
    "            for bar in bars:\n",
    "                height = bar.get_height()\n",
    "                ax.text(bar.get_x() + bar.get_width()/2, height,\n",
    "                       f'{height:.1f}', ha='center', va='bottom')\n",
    "        \n",
    "        ax.set_title(f'Average {metric.replace(\"_\", \" \")}')\n",
    "        ax.set_ylabel(ylabel)\n",
    "        ax.set_xticks(x)\n",
    "        ax.set_xticklabels([m.replace('_', '\\n') for m in models], rotation=45, ha='right')\n",
    "        ax.legend()\n",
    "        ax.grid(True, alpha=0.3)\n",
    "    \n",
    "    plt.suptitle('Performance Metrics Comparison\\nAveraged Across All Periods and Currencies', \n",
    "                 fontsize=16, y=1.02)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('aggregate_metrics_comparison.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b7a88a2-9638-444e-bc2f-b24f768614e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8f89b1cd-c81a-4802-8d86-f5bdc18928b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import yfinance as yf\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from xgboost import XGBClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a530d5f9-9810-4aa8-ab4a-56fcf22a913b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d65d4509-593c-4485-9f9e-e207a16dc165",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Analyzing EURUSD=X_2018\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -2.37%\n",
      "Sortino: -1.96\n",
      "Max Drawdown: -3.20%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -2.02%\n",
      "Sortino: -1.71\n",
      "Max Drawdown: -3.09%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 10.81%\n",
      "Sortino: 7.40\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 9.82%\n",
      "Sortino: 8.38\n",
      "Max Drawdown: -1.11%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 6.06%\n",
      "Sortino: 3.13\n",
      "Max Drawdown: -1.06%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 0.57%\n",
      "Sortino: -0.21\n",
      "Max Drawdown: -2.41%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 2.77%\n",
      "Sortino: 1.13\n",
      "Max Drawdown: -2.48%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -3.42%\n",
      "Sortino: -2.59\n",
      "Max Drawdown: -7.02%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: -0.05%\n",
      "Sortino: -0.51\n",
      "Max Drawdown: -2.43%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 0.55%\n",
      "Sortino: -0.22\n",
      "Max Drawdown: -3.81%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 11.01%\n",
      "Sortino: 6.77\n",
      "Max Drawdown: -1.05%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7619\n",
      "Total Return: 13.37%\n",
      "Sortino: 12.27\n",
      "Max Drawdown: -0.62%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -1.33%\n",
      "Sortino: -1.37\n",
      "Max Drawdown: -3.02%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 1.22%\n",
      "Sortino: 0.23\n",
      "Max Drawdown: -2.50%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -1.90%\n",
      "Sortino: -1.61\n",
      "Max Drawdown: -2.74%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 2.05%\n",
      "Sortino: 0.69\n",
      "Max Drawdown: -2.46%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: -2.22%\n",
      "Sortino: -1.76\n",
      "Max Drawdown: -4.28%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -0.73%\n",
      "Sortino: -1.03\n",
      "Max Drawdown: -3.85%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: 1.44%\n",
      "Sortino: 0.35\n",
      "Max Drawdown: -1.99%\n",
      "Number of Trades: 5\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 1.91%\n",
      "Sortino: 0.71\n",
      "Max Drawdown: -2.07%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -2.42%\n",
      "Sortino: -1.96\n",
      "Max Drawdown: -3.20%\n",
      "Number of Trades: 1\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -2.07%\n",
      "Sortino: -1.71\n",
      "Max Drawdown: -3.09%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 2.33%\n",
      "Sortino: 0.79\n",
      "Max Drawdown: -2.10%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -2.38%\n",
      "Sortino: -2.05\n",
      "Max Drawdown: -5.97%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 10.88%\n",
      "Sortino: 7.51\n",
      "Max Drawdown: -1.12%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 2.96%\n",
      "Sortino: 1.40\n",
      "Max Drawdown: -2.33%\n",
      "Number of Trades: 12\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -2.42%\n",
      "Sortino: -1.96\n",
      "Max Drawdown: -3.20%\n",
      "Number of Trades: 1\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -2.07%\n",
      "Sortino: -1.71\n",
      "Max Drawdown: -3.09%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -2.15%\n",
      "Sortino: -1.83\n",
      "Max Drawdown: -4.25%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -3.67%\n",
      "Sortino: -2.60\n",
      "Max Drawdown: -4.58%\n",
      "Number of Trades: 11\n",
      "\n",
      "Analyzing EURUSD=X_2019\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (259, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (259, 9)\n",
      "Enhanced features shape before cleaning: (259, 9)\n",
      "Features shape after cleaning: (258, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 1.45%\n",
      "Sortino: 0.38\n",
      "Max Drawdown: -1.45%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -0.73%\n",
      "Sortino: -0.64\n",
      "Max Drawdown: -6.50%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 5.87%\n",
      "Sortino: 6.59\n",
      "Max Drawdown: -0.84%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8413\n",
      "Total Return: 22.32%\n",
      "Sortino: 16.45\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -1.59%\n",
      "Sortino: -2.46\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 4.69%\n",
      "Sortino: 2.09\n",
      "Max Drawdown: -2.57%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 0.80%\n",
      "Sortino: -0.15\n",
      "Max Drawdown: -1.65%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 2.65%\n",
      "Sortino: 0.65\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 2.00%\n",
      "Sortino: 0.98\n",
      "Max Drawdown: -1.42%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 11.37%\n",
      "Sortino: 7.31\n",
      "Max Drawdown: -1.95%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 6.77%\n",
      "Sortino: 7.92\n",
      "Max Drawdown: -0.84%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7619\n",
      "Total Return: 11.25%\n",
      "Sortino: 3.16\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 3.15%\n",
      "Sortino: 2.54\n",
      "Max Drawdown: -0.79%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 5.68%\n",
      "Sortino: 1.98\n",
      "Max Drawdown: -3.25%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 0.72%\n",
      "Sortino: -0.22\n",
      "Max Drawdown: -2.64%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 3.11%\n",
      "Sortino: 0.83\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 2.34%\n",
      "Sortino: 1.33\n",
      "Max Drawdown: -1.53%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 3.11%\n",
      "Sortino: 0.87\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 12\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 1.54%\n",
      "Sortino: 0.58\n",
      "Max Drawdown: -1.76%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -1.70%\n",
      "Sortino: -0.88\n",
      "Max Drawdown: -7.73%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.41%\n",
      "Sortino: -1.36\n",
      "Max Drawdown: -2.80%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 4.23%\n",
      "Sortino: 1.29\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 3.54%\n",
      "Sortino: 2.77\n",
      "Max Drawdown: -0.79%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 6.33%\n",
      "Sortino: 2.02\n",
      "Max Drawdown: -3.40%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 5.59%\n",
      "Sortino: 5.67\n",
      "Max Drawdown: -0.55%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7143\n",
      "Total Return: 8.09%\n",
      "Sortino: 2.42\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 0.92%\n",
      "Sortino: -0.04\n",
      "Max Drawdown: -1.36%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 7.41%\n",
      "Sortino: 2.21\n",
      "Max Drawdown: -4.72%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.3538\n",
      "Total Return: -2.57%\n",
      "Sortino: -3.69\n",
      "Max Drawdown: -3.63%\n",
      "Number of Trades: 3\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -4.63%\n",
      "Sortino: -2.00\n",
      "Max Drawdown: -7.55%\n",
      "Number of Trades: 9\n",
      "\n",
      "Analyzing EURUSD=X_2020\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (261, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (261, 9)\n",
      "Enhanced features shape before cleaning: (261, 9)\n",
      "Features shape after cleaning: (260, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 4.10%\n",
      "Sortino: 2.52\n",
      "Max Drawdown: -1.79%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -4.30%\n",
      "Sortino: -3.28\n",
      "Max Drawdown: -5.00%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 9.24%\n",
      "Sortino: 7.68\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 6.93%\n",
      "Sortino: 4.26\n",
      "Max Drawdown: -1.44%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 1.83%\n",
      "Sortino: 0.61\n",
      "Max Drawdown: -2.81%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -2.24%\n",
      "Sortino: -1.96\n",
      "Max Drawdown: -4.27%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.11%\n",
      "Sortino: -0.67\n",
      "Max Drawdown: -3.45%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 1.85%\n",
      "Sortino: 0.66\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 6\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 1.64%\n",
      "Sortino: 0.48\n",
      "Max Drawdown: -2.67%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 4.97%\n",
      "Sortino: 3.09\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 10\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 11.54%\n",
      "Sortino: 10.36\n",
      "Max Drawdown: -1.06%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 11.53%\n",
      "Sortino: 8.89\n",
      "Max Drawdown: -0.73%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 0.61%\n",
      "Sortino: -0.20\n",
      "Max Drawdown: -2.67%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 5.40%\n",
      "Sortino: 3.43\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 8\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -2.01%\n",
      "Sortino: -1.88\n",
      "Max Drawdown: -3.72%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -3.08%\n",
      "Sortino: -2.33\n",
      "Max Drawdown: -3.42%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -3.16%\n",
      "Sortino: -2.49\n",
      "Max Drawdown: -4.27%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 0.05%\n",
      "Sortino: -0.52\n",
      "Max Drawdown: -2.30%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -2.22%\n",
      "Sortino: -1.91\n",
      "Max Drawdown: -3.65%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 2.60%\n",
      "Sortino: 1.14\n",
      "Max Drawdown: -2.15%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.54%\n",
      "Sortino: -0.93\n",
      "Max Drawdown: -3.87%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -0.39%\n",
      "Sortino: -0.82\n",
      "Max Drawdown: -2.46%\n",
      "Number of Trades: 14\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 0.26%\n",
      "Sortino: -0.43\n",
      "Max Drawdown: -2.98%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 1.18%\n",
      "Sortino: 0.19\n",
      "Max Drawdown: -2.46%\n",
      "Number of Trades: 10\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 8.06%\n",
      "Sortino: 6.17\n",
      "Max Drawdown: -1.55%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 4.84%\n",
      "Sortino: 2.75\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 5\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -0.01%\n",
      "Sortino: -0.59\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 3.11%\n",
      "Sortino: 1.57\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 4\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -4.97%\n",
      "Sortino: -3.33\n",
      "Max Drawdown: -5.15%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -1.74%\n",
      "Sortino: -1.64\n",
      "Max Drawdown: -2.78%\n",
      "Number of Trades: 30\n",
      "\n",
      "Analyzing EURUSD=X_2021\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -2.47%\n",
      "Sortino: -2.11\n",
      "Max Drawdown: -4.11%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -1.24%\n",
      "Sortino: -1.06\n",
      "Max Drawdown: -5.17%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 11.33%\n",
      "Sortino: 13.50\n",
      "Max Drawdown: -0.46%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 15.14%\n",
      "Sortino: 11.95\n",
      "Max Drawdown: -0.67%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 0.90%\n",
      "Sortino: -0.02\n",
      "Max Drawdown: -2.63%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -3.72%\n",
      "Sortino: -2.33\n",
      "Max Drawdown: -3.95%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 1.85%\n",
      "Sortino: 0.69\n",
      "Max Drawdown: -1.54%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 0.22%\n",
      "Sortino: -0.31\n",
      "Max Drawdown: -2.65%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 1.13%\n",
      "Sortino: 0.13\n",
      "Max Drawdown: -3.20%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: -4.50%\n",
      "Sortino: -2.58\n",
      "Max Drawdown: -7.58%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 4.80%\n",
      "Sortino: 2.87\n",
      "Max Drawdown: -2.16%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 10.06%\n",
      "Sortino: 6.08\n",
      "Max Drawdown: -1.28%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -2.79%\n",
      "Sortino: -2.24\n",
      "Max Drawdown: -4.30%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -1.24%\n",
      "Sortino: -0.99\n",
      "Max Drawdown: -4.95%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 2.40%\n",
      "Sortino: 1.18\n",
      "Max Drawdown: -1.88%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -3.95%\n",
      "Sortino: -2.25\n",
      "Max Drawdown: -4.06%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 1.93%\n",
      "Sortino: 0.78\n",
      "Max Drawdown: -1.27%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 1.04%\n",
      "Sortino: 0.09\n",
      "Max Drawdown: -2.71%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -3.96%\n",
      "Sortino: -3.01\n",
      "Max Drawdown: -4.82%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -0.04%\n",
      "Sortino: -0.42\n",
      "Max Drawdown: -4.50%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 2.43%\n",
      "Sortino: 1.24\n",
      "Max Drawdown: -1.54%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: -1.37%\n",
      "Sortino: -0.98\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 0.20%\n",
      "Sortino: -0.46\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 8\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -1.35%\n",
      "Sortino: -1.09\n",
      "Max Drawdown: -5.49%\n",
      "Number of Trades: 4\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 4.60%\n",
      "Sortino: 2.52\n",
      "Max Drawdown: -2.08%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 12.46%\n",
      "Sortino: 8.93\n",
      "Max Drawdown: -1.42%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 1.76%\n",
      "Sortino: 0.57\n",
      "Max Drawdown: -1.64%\n",
      "Number of Trades: 9\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -4.91%\n",
      "Sortino: -2.74\n",
      "Max Drawdown: -5.85%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 0.66%\n",
      "Sortino: -0.21\n",
      "Max Drawdown: -1.95%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -0.81%\n",
      "Sortino: -0.79\n",
      "Max Drawdown: -3.31%\n",
      "Number of Trades: 13\n",
      "\n",
      "Analyzing EURUSD=X_2022\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 8.14%\n",
      "Sortino: 3.26\n",
      "Max Drawdown: -3.39%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 1.55%\n",
      "Sortino: 0.27\n",
      "Max Drawdown: -4.21%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 16.57%\n",
      "Sortino: 6.91\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 14.56%\n",
      "Sortino: 7.28\n",
      "Max Drawdown: -1.43%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 4.70%\n",
      "Sortino: 1.38\n",
      "Max Drawdown: -2.73%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: 3.92%\n",
      "Sortino: 1.68\n",
      "Max Drawdown: -3.48%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -1.31%\n",
      "Sortino: -0.71\n",
      "Max Drawdown: -4.61%\n",
      "Number of Trades: 39\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: 0.85%\n",
      "Sortino: 0.01\n",
      "Max Drawdown: -4.09%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 4.73%\n",
      "Sortino: 1.42\n",
      "Max Drawdown: -3.69%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: 2.67%\n",
      "Sortino: 1.02\n",
      "Max Drawdown: -4.55%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 19.93%\n",
      "Sortino: 7.88\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 17.28%\n",
      "Sortino: 12.29\n",
      "Max Drawdown: -1.42%\n",
      "Number of Trades: 41\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: -0.45%\n",
      "Sortino: -0.40\n",
      "Max Drawdown: -4.30%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: -2.28%\n",
      "Sortino: -1.43\n",
      "Max Drawdown: -5.18%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -2.57%\n",
      "Sortino: -1.16\n",
      "Max Drawdown: -7.97%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: 3.16%\n",
      "Sortino: 1.44\n",
      "Max Drawdown: -4.55%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -1.44%\n",
      "Sortino: -0.79\n",
      "Max Drawdown: -6.14%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 8.21%\n",
      "Sortino: 3.39\n",
      "Max Drawdown: -3.12%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 9.17%\n",
      "Sortino: 3.20\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -1.43%\n",
      "Sortino: -1.15\n",
      "Max Drawdown: -5.62%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: 5.72%\n",
      "Sortino: 2.14\n",
      "Max Drawdown: -3.95%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4127\n",
      "Total Return: -1.32%\n",
      "Sortino: -1.12\n",
      "Max Drawdown: -4.87%\n",
      "Number of Trades: 9\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -0.98%\n",
      "Sortino: -0.62\n",
      "Max Drawdown: -7.23%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 9.25%\n",
      "Sortino: 5.46\n",
      "Max Drawdown: -2.62%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 22.99%\n",
      "Sortino: 8.77\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 14.96%\n",
      "Sortino: 8.36\n",
      "Max Drawdown: -1.40%\n",
      "Number of Trades: 39\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -0.98%\n",
      "Sortino: -0.62\n",
      "Max Drawdown: -5.00%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -1.78%\n",
      "Sortino: -1.25\n",
      "Max Drawdown: -4.51%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 3.74%\n",
      "Sortino: 1.07\n",
      "Max Drawdown: -4.82%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 2.53%\n",
      "Sortino: 0.78\n",
      "Max Drawdown: -3.05%\n",
      "Number of Trades: 22\n",
      "\n",
      "Analyzing EURUSD=X_2023\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 5.17%\n",
      "Sortino: 2.44\n",
      "Max Drawdown: -2.18%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -2.04%\n",
      "Sortino: -2.18\n",
      "Max Drawdown: -2.48%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8462\n",
      "Total Return: 18.97%\n",
      "Sortino: 14.24\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 10.11%\n",
      "Sortino: 11.03\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 3.79%\n",
      "Sortino: 1.79\n",
      "Max Drawdown: -2.74%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 5.88%\n",
      "Sortino: 4.90\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 8.29%\n",
      "Sortino: 4.81\n",
      "Max Drawdown: -1.34%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 4.51%\n",
      "Sortino: 3.46\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 10.06%\n",
      "Sortino: 5.78\n",
      "Max Drawdown: -1.52%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 7.16%\n",
      "Sortino: 6.37\n",
      "Max Drawdown: -1.05%\n",
      "Number of Trades: 37\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 14.16%\n",
      "Sortino: 10.33\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 8.03%\n",
      "Sortino: 8.39\n",
      "Max Drawdown: -0.68%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 9.56%\n",
      "Sortino: 6.56\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 0.33%\n",
      "Sortino: -0.41\n",
      "Max Drawdown: -2.13%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 8.81%\n",
      "Sortino: 5.77\n",
      "Max Drawdown: -1.16%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 2.53%\n",
      "Sortino: 1.38\n",
      "Max Drawdown: -1.20%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 13.61%\n",
      "Sortino: 8.84\n",
      "Max Drawdown: -0.93%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 10.52%\n",
      "Sortino: 14.72\n",
      "Max Drawdown: -0.46%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 0.78%\n",
      "Sortino: -0.06\n",
      "Max Drawdown: -2.07%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 4.25%\n",
      "Sortino: 2.95\n",
      "Max Drawdown: -1.54%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 4.00%\n",
      "Sortino: 2.10\n",
      "Max Drawdown: -2.97%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 3.48%\n",
      "Sortino: 2.36\n",
      "Max Drawdown: -1.25%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 7.22%\n",
      "Sortino: 4.02\n",
      "Max Drawdown: -2.08%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 4.59%\n",
      "Sortino: 3.21\n",
      "Max Drawdown: -1.20%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 11.78%\n",
      "Sortino: 8.87\n",
      "Max Drawdown: -1.41%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 5.55%\n",
      "Sortino: 4.72\n",
      "Max Drawdown: -0.68%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 5.74%\n",
      "Sortino: 2.47\n",
      "Max Drawdown: -2.25%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 0.96%\n",
      "Sortino: 0.04\n",
      "Max Drawdown: -1.92%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.18%\n",
      "Sortino: -0.61\n",
      "Max Drawdown: -3.08%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 4.09%\n",
      "Sortino: 3.14\n",
      "Max Drawdown: -0.83%\n",
      "Number of Trades: 16\n",
      "\n",
      "Analyzing EURUSD=X_2018-19\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -2.32%\n",
      "Sortino: -1.65\n",
      "Max Drawdown: -3.48%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 0.04%\n",
      "Sortino: -0.41\n",
      "Max Drawdown: -6.50%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7615\n",
      "Total Return: 22.27%\n",
      "Sortino: 15.19\n",
      "Max Drawdown: -0.46%\n",
      "Number of Trades: 60\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8438\n",
      "Total Return: 48.61%\n",
      "Sortino: 18.65\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 44\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -0.57%\n",
      "Sortino: -1.07\n",
      "Max Drawdown: -5.44%\n",
      "Number of Trades: 53\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 1.37%\n",
      "Sortino: -0.09\n",
      "Max Drawdown: -6.56%\n",
      "Number of Trades: 43\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 0.31%\n",
      "Sortino: -0.63\n",
      "Max Drawdown: -2.49%\n",
      "Number of Trades: 58\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: 4.86%\n",
      "Sortino: 0.66\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: -0.67%\n",
      "Sortino: -1.09\n",
      "Max Drawdown: -4.25%\n",
      "Number of Trades: 70\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 3.22%\n",
      "Sortino: 0.30\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 18.92%\n",
      "Sortino: 10.51\n",
      "Max Drawdown: -0.88%\n",
      "Number of Trades: 51\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7344\n",
      "Total Return: 37.29%\n",
      "Sortino: 12.55\n",
      "Max Drawdown: -1.17%\n",
      "Number of Trades: 47\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 4.23%\n",
      "Sortino: 1.08\n",
      "Max Drawdown: -2.64%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 0.23%\n",
      "Sortino: -0.35\n",
      "Max Drawdown: -6.50%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 2.68%\n",
      "Sortino: 0.30\n",
      "Max Drawdown: -2.02%\n",
      "Number of Trades: 62\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5859\n",
      "Total Return: 4.05%\n",
      "Sortino: 0.46\n",
      "Max Drawdown: -6.06%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5923\n",
      "Total Return: 6.54%\n",
      "Sortino: 1.94\n",
      "Max Drawdown: -2.38%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5703\n",
      "Total Return: 3.23%\n",
      "Sortino: 0.33\n",
      "Max Drawdown: -5.92%\n",
      "Number of Trades: 46\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 0.79%\n",
      "Sortino: -0.43\n",
      "Max Drawdown: -4.80%\n",
      "Number of Trades: 52\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6406\n",
      "Total Return: 12.45%\n",
      "Sortino: 2.44\n",
      "Max Drawdown: -3.03%\n",
      "Number of Trades: 49\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: -0.71%\n",
      "Sortino: -1.03\n",
      "Max Drawdown: -3.12%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 3.53%\n",
      "Sortino: 0.39\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 9\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 3.91%\n",
      "Sortino: 0.80\n",
      "Max Drawdown: -2.27%\n",
      "Number of Trades: 48\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5781\n",
      "Total Return: 6.27%\n",
      "Sortino: 0.96\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 12.72%\n",
      "Sortino: 5.52\n",
      "Max Drawdown: -1.18%\n",
      "Number of Trades: 66\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7500\n",
      "Total Return: 38.53%\n",
      "Sortino: 13.98\n",
      "Max Drawdown: -1.17%\n",
      "Number of Trades: 39\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4692\n",
      "Total Return: 0.91%\n",
      "Sortino: -0.47\n",
      "Max Drawdown: -4.09%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5234\n",
      "Total Return: 0.90%\n",
      "Sortino: -0.21\n",
      "Max Drawdown: -5.14%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -2.59%\n",
      "Sortino: -1.78\n",
      "Max Drawdown: -4.13%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5234\n",
      "Total Return: 3.33%\n",
      "Sortino: 0.33\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 11\n",
      "\n",
      "Analyzing EURUSD=X_2020-21\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (522, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (522, 9)\n",
      "Enhanced features shape before cleaning: (522, 9)\n",
      "Features shape after cleaning: (521, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -4.57%\n",
      "Sortino: -2.32\n",
      "Max Drawdown: -5.80%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -7.57%\n",
      "Sortino: -2.17\n",
      "Max Drawdown: -9.40%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7308\n",
      "Total Return: 20.66%\n",
      "Sortino: 11.75\n",
      "Max Drawdown: -0.96%\n",
      "Number of Trades: 53\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7656\n",
      "Total Return: 47.40%\n",
      "Sortino: 14.32\n",
      "Max Drawdown: -1.13%\n",
      "Number of Trades: 49\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5154\n",
      "Total Return: 0.61%\n",
      "Sortino: -0.56\n",
      "Max Drawdown: -2.87%\n",
      "Number of Trades: 58\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: 5.71%\n",
      "Sortino: 0.87\n",
      "Max Drawdown: -4.49%\n",
      "Number of Trades: 52\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 5.85%\n",
      "Sortino: 1.49\n",
      "Max Drawdown: -3.98%\n",
      "Number of Trades: 52\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5703\n",
      "Total Return: 5.83%\n",
      "Sortino: 0.89\n",
      "Max Drawdown: -3.70%\n",
      "Number of Trades: 34\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5462\n",
      "Total Return: 1.72%\n",
      "Sortino: -0.08\n",
      "Max Drawdown: -3.10%\n",
      "Number of Trades: 67\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5703\n",
      "Total Return: 4.53%\n",
      "Sortino: 0.58\n",
      "Max Drawdown: -3.93%\n",
      "Number of Trades: 36\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7769\n",
      "Total Return: 20.83%\n",
      "Sortino: 11.48\n",
      "Max Drawdown: -0.79%\n",
      "Number of Trades: 68\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7500\n",
      "Total Return: 42.79%\n",
      "Sortino: 11.04\n",
      "Max Drawdown: -1.04%\n",
      "Number of Trades: 47\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -7.70%\n",
      "Sortino: -3.42\n",
      "Max Drawdown: -8.14%\n",
      "Number of Trades: 74\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5781\n",
      "Total Return: 3.86%\n",
      "Sortino: 0.46\n",
      "Max Drawdown: -4.80%\n",
      "Number of Trades: 44\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 4.52%\n",
      "Sortino: 1.00\n",
      "Max Drawdown: -2.95%\n",
      "Number of Trades: 60\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5625\n",
      "Total Return: 4.86%\n",
      "Sortino: 0.68\n",
      "Max Drawdown: -5.39%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: 4.34%\n",
      "Sortino: 0.86\n",
      "Max Drawdown: -3.14%\n",
      "Number of Trades: 54\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: 14.53%\n",
      "Sortino: 3.18\n",
      "Max Drawdown: -3.11%\n",
      "Number of Trades: 59\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -3.12%\n",
      "Sortino: -1.89\n",
      "Max Drawdown: -4.01%\n",
      "Number of Trades: 64\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5391\n",
      "Total Return: 0.40%\n",
      "Sortino: -0.29\n",
      "Max Drawdown: -5.06%\n",
      "Number of Trades: 51\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5769\n",
      "Total Return: 2.61%\n",
      "Sortino: 0.23\n",
      "Max Drawdown: -3.51%\n",
      "Number of Trades: 46\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: 3.55%\n",
      "Sortino: 0.40\n",
      "Max Drawdown: -4.49%\n",
      "Number of Trades: 43\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 4.87%\n",
      "Sortino: 1.13\n",
      "Max Drawdown: -3.56%\n",
      "Number of Trades: 60\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5859\n",
      "Total Return: 10.76%\n",
      "Sortino: 2.08\n",
      "Max Drawdown: -3.79%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7462\n",
      "Total Return: 17.86%\n",
      "Sortino: 9.31\n",
      "Max Drawdown: -1.53%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7109\n",
      "Total Return: 36.10%\n",
      "Sortino: 8.47\n",
      "Max Drawdown: -2.29%\n",
      "Number of Trades: 44\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -6.22%\n",
      "Sortino: -2.74\n",
      "Max Drawdown: -6.39%\n",
      "Number of Trades: 61\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5391\n",
      "Total Return: -0.64%\n",
      "Sortino: -0.52\n",
      "Max Drawdown: -5.73%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: -2.52%\n",
      "Sortino: -1.48\n",
      "Max Drawdown: -5.54%\n",
      "Number of Trades: 51\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: 1.02%\n",
      "Sortino: -0.17\n",
      "Max Drawdown: -6.39%\n",
      "Number of Trades: 59\n",
      "\n",
      "Analyzing EURUSD=X_2022-23\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 2.21%\n",
      "Sortino: 0.08\n",
      "Max Drawdown: -6.81%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -3.25%\n",
      "Sortino: -1.80\n",
      "Max Drawdown: -3.28%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7462\n",
      "Total Return: 33.41%\n",
      "Sortino: 12.95\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8047\n",
      "Total Return: 26.19%\n",
      "Sortino: 14.63\n",
      "Max Drawdown: -0.75%\n",
      "Number of Trades: 61\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 22.11%\n",
      "Sortino: 7.29\n",
      "Max Drawdown: -1.96%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6562\n",
      "Total Return: 11.84%\n",
      "Sortino: 4.12\n",
      "Max Drawdown: -1.84%\n",
      "Number of Trades: 55\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6538\n",
      "Total Return: 20.08%\n",
      "Sortino: 5.99\n",
      "Max Drawdown: -1.22%\n",
      "Number of Trades: 62\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6875\n",
      "Total Return: 12.33%\n",
      "Sortino: 4.61\n",
      "Max Drawdown: -1.33%\n",
      "Number of Trades: 57\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 12.49%\n",
      "Sortino: 3.09\n",
      "Max Drawdown: -2.17%\n",
      "Number of Trades: 73\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6953\n",
      "Total Return: 12.06%\n",
      "Sortino: 4.52\n",
      "Max Drawdown: -1.33%\n",
      "Number of Trades: 63\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7154\n",
      "Total Return: 33.21%\n",
      "Sortino: 13.81\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 69\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7734\n",
      "Total Return: 23.35%\n",
      "Sortino: 11.46\n",
      "Max Drawdown: -0.68%\n",
      "Number of Trades: 53\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 4.48%\n",
      "Sortino: 0.73\n",
      "Max Drawdown: -2.63%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6016\n",
      "Total Return: 4.51%\n",
      "Sortino: 0.98\n",
      "Max Drawdown: -3.51%\n",
      "Number of Trades: 63\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 21.82%\n",
      "Sortino: 6.66\n",
      "Max Drawdown: -1.08%\n",
      "Number of Trades: 67\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6562\n",
      "Total Return: 9.52%\n",
      "Sortino: 3.09\n",
      "Max Drawdown: -1.98%\n",
      "Number of Trades: 67\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5923\n",
      "Total Return: 9.07%\n",
      "Sortino: 1.95\n",
      "Max Drawdown: -3.42%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6875\n",
      "Total Return: 15.88%\n",
      "Sortino: 5.82\n",
      "Max Drawdown: -1.05%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 5.31%\n",
      "Sortino: 0.92\n",
      "Max Drawdown: -2.98%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6094\n",
      "Total Return: 10.59%\n",
      "Sortino: 3.83\n",
      "Max Drawdown: -1.12%\n",
      "Number of Trades: 36\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6077\n",
      "Total Return: 15.10%\n",
      "Sortino: 4.05\n",
      "Max Drawdown: -3.24%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6797\n",
      "Total Return: 14.35%\n",
      "Sortino: 5.61\n",
      "Max Drawdown: -0.97%\n",
      "Number of Trades: 47\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6538\n",
      "Total Return: 21.13%\n",
      "Sortino: 6.06\n",
      "Max Drawdown: -2.41%\n",
      "Number of Trades: 69\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7266\n",
      "Total Return: 15.95%\n",
      "Sortino: 6.27\n",
      "Max Drawdown: -0.93%\n",
      "Number of Trades: 55\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 29.72%\n",
      "Sortino: 10.61\n",
      "Max Drawdown: -1.01%\n",
      "Number of Trades: 63\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7344\n",
      "Total Return: 21.17%\n",
      "Sortino: 10.08\n",
      "Max Drawdown: -0.68%\n",
      "Number of Trades: 53\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 2.36%\n",
      "Sortino: 0.14\n",
      "Max Drawdown: -3.41%\n",
      "Number of Trades: 39\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5938\n",
      "Total Return: 4.73%\n",
      "Sortino: 1.14\n",
      "Max Drawdown: -3.28%\n",
      "Number of Trades: 52\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5923\n",
      "Total Return: 9.94%\n",
      "Sortino: 2.36\n",
      "Max Drawdown: -3.11%\n",
      "Number of Trades: 43\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6562\n",
      "Total Return: 11.08%\n",
      "Sortino: 3.55\n",
      "Max Drawdown: -1.50%\n",
      "Number of Trades: 51\n",
      "\n",
      "Analyzing EURUSD=X_2018-23\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (1564, 6)\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (269, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (1564, 9)\n",
      "Enhanced features shape before cleaning: (1564, 9)\n",
      "Features shape after cleaning: (1563, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for EURUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 6.34%\n",
      "Sortino: 0.04\n",
      "Max Drawdown: -8.41%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -6.56%\n",
      "Sortino: -1.63\n",
      "Max Drawdown: -8.25%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8082\n",
      "Total Return: 269.76%\n",
      "Sortino: 21.30\n",
      "Max Drawdown: -0.91%\n",
      "Number of Trades: 188\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8015\n",
      "Total Return: 74.93%\n",
      "Sortino: 18.14\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 139\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5934\n",
      "Total Return: 19.56%\n",
      "Sortino: 0.87\n",
      "Max Drawdown: -15.24%\n",
      "Number of Trades: 153\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6105\n",
      "Total Return: 25.45%\n",
      "Sortino: 3.41\n",
      "Max Drawdown: -2.37%\n",
      "Number of Trades: 106\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5652\n",
      "Total Return: 23.08%\n",
      "Sortino: 1.17\n",
      "Max Drawdown: -8.41%\n",
      "Number of Trades: 115\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6105\n",
      "Total Return: 21.04%\n",
      "Sortino: 2.59\n",
      "Max Drawdown: -2.78%\n",
      "Number of Trades: 90\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6215\n",
      "Total Return: 52.38%\n",
      "Sortino: 2.90\n",
      "Max Drawdown: -4.33%\n",
      "Number of Trades: 131\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6142\n",
      "Total Return: 29.27%\n",
      "Sortino: 4.36\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 120\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7775\n",
      "Total Return: 237.29%\n",
      "Sortino: 16.09\n",
      "Max Drawdown: -1.46%\n",
      "Number of Trades: 206\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7678\n",
      "Total Return: 69.40%\n",
      "Sortino: 12.42\n",
      "Max Drawdown: -1.38%\n",
      "Number of Trades: 131\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5882\n",
      "Total Return: 33.17%\n",
      "Sortino: 1.72\n",
      "Max Drawdown: -6.14%\n",
      "Number of Trades: 128\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6105\n",
      "Total Return: 31.45%\n",
      "Sortino: 4.90\n",
      "Max Drawdown: -1.58%\n",
      "Number of Trades: 102\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6240\n",
      "Total Return: 59.03%\n",
      "Sortino: 3.15\n",
      "Max Drawdown: -7.83%\n",
      "Number of Trades: 129\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6142\n",
      "Total Return: 27.43%\n",
      "Sortino: 3.84\n",
      "Max Drawdown: -2.51%\n",
      "Number of Trades: 102\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7340\n",
      "Total Return: 176.52%\n",
      "Sortino: 9.87\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 150\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6704\n",
      "Total Return: 48.85%\n",
      "Sortino: 8.13\n",
      "Max Drawdown: -1.59%\n",
      "Number of Trades: 77\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5575\n",
      "Total Return: 19.41%\n",
      "Sortino: 0.92\n",
      "Max Drawdown: -8.41%\n",
      "Number of Trades: 80\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5918\n",
      "Total Return: 11.68%\n",
      "Sortino: 1.16\n",
      "Max Drawdown: -3.24%\n",
      "Number of Trades: 44\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5269\n",
      "Total Return: 1.52%\n",
      "Sortino: -0.30\n",
      "Max Drawdown: -8.41%\n",
      "Number of Trades: 117\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5581\n",
      "Total Return: 9.28%\n",
      "Sortino: 0.80\n",
      "Max Drawdown: -5.07%\n",
      "Number of Trades: 100\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6113\n",
      "Total Return: 44.31%\n",
      "Sortino: 2.35\n",
      "Max Drawdown: -6.14%\n",
      "Number of Trades: 143\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6217\n",
      "Total Return: 27.18%\n",
      "Sortino: 3.79\n",
      "Max Drawdown: -2.16%\n",
      "Number of Trades: 115\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7775\n",
      "Total Return: 240.80%\n",
      "Sortino: 18.14\n",
      "Max Drawdown: -1.79%\n",
      "Number of Trades: 212\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7715\n",
      "Total Return: 71.33%\n",
      "Sortino: 16.78\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 133\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5243\n",
      "Total Return: 5.65%\n",
      "Sortino: -0.00\n",
      "Max Drawdown: -8.41%\n",
      "Number of Trades: 69\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4944\n",
      "Total Return: 1.06%\n",
      "Sortino: -0.46\n",
      "Max Drawdown: -7.21%\n",
      "Number of Trades: 84\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5422\n",
      "Total Return: 1.30%\n",
      "Sortino: -0.29\n",
      "Max Drawdown: -7.74%\n",
      "Number of Trades: 144\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5131\n",
      "Total Return: -4.32%\n",
      "Sortino: -1.22\n",
      "Max Drawdown: -7.65%\n",
      "Number of Trades: 89\n",
      "\n",
      "Analyzing AUDUSD=X_2018\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -1.81%\n",
      "Sortino: -1.15\n",
      "Max Drawdown: -4.43%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 0.50%\n",
      "Sortino: -0.15\n",
      "Max Drawdown: -3.41%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 17.23%\n",
      "Sortino: 10.13\n",
      "Max Drawdown: -1.67%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 17.07%\n",
      "Sortino: 9.17\n",
      "Max Drawdown: -1.90%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 2.78%\n",
      "Sortino: 0.97\n",
      "Max Drawdown: -2.91%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 9.57%\n",
      "Sortino: 3.70\n",
      "Max Drawdown: -1.71%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 6.92%\n",
      "Sortino: 3.40\n",
      "Max Drawdown: -2.08%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 8.15%\n",
      "Sortino: 3.25\n",
      "Max Drawdown: -1.20%\n",
      "Number of Trades: 14\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 7.32%\n",
      "Sortino: 3.55\n",
      "Max Drawdown: -2.27%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 8.63%\n",
      "Sortino: 3.57\n",
      "Max Drawdown: -1.38%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 12.14%\n",
      "Sortino: 6.97\n",
      "Max Drawdown: -2.00%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 15.22%\n",
      "Sortino: 8.76\n",
      "Max Drawdown: -2.11%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 12.20%\n",
      "Sortino: 6.80\n",
      "Max Drawdown: -2.27%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 5.16%\n",
      "Sortino: 1.86\n",
      "Max Drawdown: -1.38%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: 2.59%\n",
      "Sortino: 0.90\n",
      "Max Drawdown: -3.44%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 3.83%\n",
      "Sortino: 1.17\n",
      "Max Drawdown: -2.43%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 0.04%\n",
      "Sortino: -0.35\n",
      "Max Drawdown: -4.11%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 2.17%\n",
      "Sortino: 0.48\n",
      "Max Drawdown: -2.73%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -2.03%\n",
      "Sortino: -1.38\n",
      "Max Drawdown: -3.00%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 5.03%\n",
      "Sortino: 1.72\n",
      "Max Drawdown: -3.35%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -1.71%\n",
      "Sortino: -1.09\n",
      "Max Drawdown: -3.75%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: -0.09%\n",
      "Sortino: -0.36\n",
      "Max Drawdown: -2.81%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 5.77%\n",
      "Sortino: 2.68\n",
      "Max Drawdown: -2.49%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 0.08%\n",
      "Sortino: -0.28\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 12.65%\n",
      "Sortino: 7.04\n",
      "Max Drawdown: -2.14%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 14.82%\n",
      "Sortino: 5.63\n",
      "Max Drawdown: -2.08%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -4.07%\n",
      "Sortino: -1.99\n",
      "Max Drawdown: -5.90%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -0.78%\n",
      "Sortino: -0.62\n",
      "Max Drawdown: -3.82%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -2.52%\n",
      "Sortino: -1.44\n",
      "Max Drawdown: -4.36%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: -1.43%\n",
      "Sortino: -0.85\n",
      "Max Drawdown: -4.05%\n",
      "Number of Trades: 30\n",
      "\n",
      "Analyzing AUDUSD=X_2019\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (259, 6)\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (259, 9)\n",
      "Enhanced features shape before cleaning: (259, 9)\n",
      "Features shape after cleaning: (258, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 2.74%\n",
      "Sortino: 1.37\n",
      "Max Drawdown: -2.21%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -11.95%\n",
      "Sortino: -3.54\n",
      "Max Drawdown: -17.78%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 13.76%\n",
      "Sortino: 15.70\n",
      "Max Drawdown: -0.54%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8254\n",
      "Total Return: 41.66%\n",
      "Sortino: 25.79\n",
      "Max Drawdown: -1.13%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 3.11%\n",
      "Sortino: 1.49\n",
      "Max Drawdown: -1.67%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: -4.39%\n",
      "Sortino: -1.29\n",
      "Max Drawdown: -14.44%\n",
      "Number of Trades: 14\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 7.04%\n",
      "Sortino: 5.44\n",
      "Max Drawdown: -3.08%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -2.06%\n",
      "Sortino: -0.66\n",
      "Max Drawdown: -14.45%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 8.95%\n",
      "Sortino: 7.98\n",
      "Max Drawdown: -1.03%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -3.10%\n",
      "Sortino: -0.90\n",
      "Max Drawdown: -14.23%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8000\n",
      "Total Return: 15.18%\n",
      "Sortino: 21.78\n",
      "Max Drawdown: -0.48%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7937\n",
      "Total Return: 30.02%\n",
      "Sortino: 8.85\n",
      "Max Drawdown: -1.67%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 2.21%\n",
      "Sortino: 0.86\n",
      "Max Drawdown: -1.32%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 15.91%\n",
      "Sortino: 4.16\n",
      "Max Drawdown: -4.78%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 5.77%\n",
      "Sortino: 3.89\n",
      "Max Drawdown: -3.47%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -7.02%\n",
      "Sortino: -1.92\n",
      "Max Drawdown: -15.51%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 11.11%\n",
      "Sortino: 8.24\n",
      "Max Drawdown: -0.93%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 9.79%\n",
      "Sortino: 1.96\n",
      "Max Drawdown: -7.81%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -0.71%\n",
      "Sortino: -1.08\n",
      "Max Drawdown: -4.64%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 7.78%\n",
      "Sortino: 1.84\n",
      "Max Drawdown: -7.27%\n",
      "Number of Trades: 11\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -2.10%\n",
      "Sortino: -1.79\n",
      "Max Drawdown: -4.38%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 18.51%\n",
      "Sortino: 4.76\n",
      "Max Drawdown: -4.78%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 9.25%\n",
      "Sortino: 7.82\n",
      "Max Drawdown: -0.99%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: -2.80%\n",
      "Sortino: -0.81\n",
      "Max Drawdown: -14.73%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7538\n",
      "Total Return: 12.41%\n",
      "Sortino: 11.90\n",
      "Max Drawdown: -1.09%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 20.81%\n",
      "Sortino: 6.05\n",
      "Max Drawdown: -2.19%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 1.03%\n",
      "Sortino: 0.07\n",
      "Max Drawdown: -2.28%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 16.91%\n",
      "Sortino: 4.19\n",
      "Max Drawdown: -4.78%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: -3.12%\n",
      "Sortino: -2.45\n",
      "Max Drawdown: -4.22%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -13.40%\n",
      "Sortino: -3.23\n",
      "Max Drawdown: -18.70%\n",
      "Number of Trades: 27\n",
      "\n",
      "Analyzing AUDUSD=X_2020\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (261, 6)\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (261, 9)\n",
      "Enhanced features shape before cleaning: (261, 9)\n",
      "Features shape after cleaning: (260, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 6.23%\n",
      "Sortino: 2.87\n",
      "Max Drawdown: -3.00%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -1.34%\n",
      "Sortino: -0.75\n",
      "Max Drawdown: -4.84%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 14.63%\n",
      "Sortino: 7.20\n",
      "Max Drawdown: -1.25%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 8.07%\n",
      "Sortino: 2.76\n",
      "Max Drawdown: -2.29%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -8.66%\n",
      "Sortino: -3.80\n",
      "Max Drawdown: -10.21%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -2.26%\n",
      "Sortino: -1.23\n",
      "Max Drawdown: -5.27%\n",
      "Number of Trades: 11\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -1.60%\n",
      "Sortino: -1.08\n",
      "Max Drawdown: -4.25%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 2.86%\n",
      "Sortino: 0.84\n",
      "Max Drawdown: -4.18%\n",
      "Number of Trades: 5\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -4.07%\n",
      "Sortino: -2.26\n",
      "Max Drawdown: -6.92%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 1.62%\n",
      "Sortino: 0.34\n",
      "Max Drawdown: -4.18%\n",
      "Number of Trades: 3\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8308\n",
      "Total Return: 20.79%\n",
      "Sortino: 14.65\n",
      "Max Drawdown: -0.78%\n",
      "Number of Trades: 36\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 15.47%\n",
      "Sortino: 7.32\n",
      "Max Drawdown: -1.59%\n",
      "Number of Trades: 41\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -6.15%\n",
      "Sortino: -2.96\n",
      "Max Drawdown: -7.27%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: 1.16%\n",
      "Sortino: 0.15\n",
      "Max Drawdown: -4.18%\n",
      "Number of Trades: 5\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -2.95%\n",
      "Sortino: -1.68\n",
      "Max Drawdown: -6.07%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 2.21%\n",
      "Sortino: 0.57\n",
      "Max Drawdown: -3.50%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -0.81%\n",
      "Sortino: -0.78\n",
      "Max Drawdown: -6.18%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -0.62%\n",
      "Sortino: -0.52\n",
      "Max Drawdown: -4.14%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: -0.59%\n",
      "Sortino: -0.56\n",
      "Max Drawdown: -3.76%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -3.89%\n",
      "Sortino: -1.78\n",
      "Max Drawdown: -7.20%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 1.99%\n",
      "Sortino: 0.50\n",
      "Max Drawdown: -4.22%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -3.34%\n",
      "Sortino: -1.55\n",
      "Max Drawdown: -7.78%\n",
      "Number of Trades: 39\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 1.16%\n",
      "Sortino: 0.13\n",
      "Max Drawdown: -5.01%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 1.46%\n",
      "Sortino: 0.26\n",
      "Max Drawdown: -3.06%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 7.86%\n",
      "Sortino: 2.96\n",
      "Max Drawdown: -2.57%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 9.99%\n",
      "Sortino: 4.62\n",
      "Max Drawdown: -1.38%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -6.21%\n",
      "Sortino: -2.97\n",
      "Max Drawdown: -7.51%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 1.62%\n",
      "Sortino: 0.34\n",
      "Max Drawdown: -4.18%\n",
      "Number of Trades: 3\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -0.98%\n",
      "Sortino: -0.86\n",
      "Max Drawdown: -5.53%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4127\n",
      "Total Return: -12.30%\n",
      "Sortino: -4.64\n",
      "Max Drawdown: -11.74%\n",
      "Number of Trades: 35\n",
      "\n",
      "Analyzing AUDUSD=X_2021\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 0.75%\n",
      "Sortino: -0.10\n",
      "Max Drawdown: -6.82%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 4.24%\n",
      "Sortino: 1.41\n",
      "Max Drawdown: -3.90%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7692\n",
      "Total Return: 18.88%\n",
      "Sortino: 11.23\n",
      "Max Drawdown: -0.98%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7143\n",
      "Total Return: 19.83%\n",
      "Sortino: 11.39\n",
      "Max Drawdown: -1.16%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: 0.10%\n",
      "Sortino: -0.43\n",
      "Max Drawdown: -3.77%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 0.70%\n",
      "Sortino: -0.05\n",
      "Max Drawdown: -3.73%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -2.01%\n",
      "Sortino: -1.57\n",
      "Max Drawdown: -4.94%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 0.73%\n",
      "Sortino: -0.04\n",
      "Max Drawdown: -3.23%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 0.68%\n",
      "Sortino: -0.12\n",
      "Max Drawdown: -4.20%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 5.10%\n",
      "Sortino: 1.71\n",
      "Max Drawdown: -2.45%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 17.62%\n",
      "Sortino: 12.11\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8413\n",
      "Total Return: 26.23%\n",
      "Sortino: 18.30\n",
      "Max Drawdown: -0.79%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -0.57%\n",
      "Sortino: -0.79\n",
      "Max Drawdown: -2.77%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -3.98%\n",
      "Sortino: -2.10\n",
      "Max Drawdown: -5.01%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 4.42%\n",
      "Sortino: 2.13\n",
      "Max Drawdown: -2.19%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 5.37%\n",
      "Sortino: 2.09\n",
      "Max Drawdown: -2.57%\n",
      "Number of Trades: 34\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 3.91%\n",
      "Sortino: 1.54\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 38\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 0.75%\n",
      "Sortino: -0.03\n",
      "Max Drawdown: -3.88%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -3.95%\n",
      "Sortino: -2.38\n",
      "Max Drawdown: -4.02%\n",
      "Number of Trades: 11\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -4.94%\n",
      "Sortino: -2.37\n",
      "Max Drawdown: -6.14%\n",
      "Number of Trades: 7\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -1.37%\n",
      "Sortino: -1.25\n",
      "Max Drawdown: -3.08%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -3.49%\n",
      "Sortino: -1.74\n",
      "Max Drawdown: -4.54%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: 0.21%\n",
      "Sortino: -0.38\n",
      "Max Drawdown: -3.04%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 7.38%\n",
      "Sortino: 3.08\n",
      "Max Drawdown: -2.57%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 12.66%\n",
      "Sortino: 7.05\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 38\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 17.69%\n",
      "Sortino: 9.81\n",
      "Max Drawdown: -2.35%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -1.75%\n",
      "Sortino: -1.39\n",
      "Max Drawdown: -2.08%\n",
      "Number of Trades: 36\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 2.87%\n",
      "Sortino: 0.84\n",
      "Max Drawdown: -2.45%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 1.10%\n",
      "Sortino: 0.10\n",
      "Max Drawdown: -1.74%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: -1.57%\n",
      "Sortino: -0.94\n",
      "Max Drawdown: -4.40%\n",
      "Number of Trades: 26\n",
      "\n",
      "Analyzing AUDUSD=X_2022\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 3.59%\n",
      "Sortino: 0.76\n",
      "Max Drawdown: -4.45%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -1.81%\n",
      "Sortino: -0.80\n",
      "Max Drawdown: -7.75%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7692\n",
      "Total Return: 39.95%\n",
      "Sortino: 14.64\n",
      "Max Drawdown: -1.16%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 31.92%\n",
      "Sortino: 21.03\n",
      "Max Drawdown: -1.33%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 7.62%\n",
      "Sortino: 1.90\n",
      "Max Drawdown: -4.52%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: 1.56%\n",
      "Sortino: 0.30\n",
      "Max Drawdown: -3.98%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 11.28%\n",
      "Sortino: 3.34\n",
      "Max Drawdown: -5.55%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 8.20%\n",
      "Sortino: 2.69\n",
      "Max Drawdown: -4.05%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: 0.02%\n",
      "Sortino: -0.20\n",
      "Max Drawdown: -8.59%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 12.53%\n",
      "Sortino: 5.53\n",
      "Max Drawdown: -3.69%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 29.37%\n",
      "Sortino: 8.74\n",
      "Max Drawdown: -2.44%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 30.85%\n",
      "Sortino: 19.92\n",
      "Max Drawdown: -1.05%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 8.98%\n",
      "Sortino: 2.61\n",
      "Max Drawdown: -4.68%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 8.38%\n",
      "Sortino: 2.68\n",
      "Max Drawdown: -5.40%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 15.50%\n",
      "Sortino: 4.78\n",
      "Max Drawdown: -5.19%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 7.83%\n",
      "Sortino: 2.58\n",
      "Max Drawdown: -3.98%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 3.75%\n",
      "Sortino: 0.72\n",
      "Max Drawdown: -8.67%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 11.56%\n",
      "Sortino: 3.39\n",
      "Max Drawdown: -4.70%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 10.35%\n",
      "Sortino: 2.95\n",
      "Max Drawdown: -4.45%\n",
      "Number of Trades: 7\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 0.68%\n",
      "Sortino: -0.02\n",
      "Max Drawdown: -6.30%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 3.74%\n",
      "Sortino: 0.85\n",
      "Max Drawdown: -6.87%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 9.55%\n",
      "Sortino: 3.01\n",
      "Max Drawdown: -2.50%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 9.55%\n",
      "Sortino: 2.75\n",
      "Max Drawdown: -4.45%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 10.97%\n",
      "Sortino: 3.60\n",
      "Max Drawdown: -3.72%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 20.68%\n",
      "Sortino: 6.04\n",
      "Max Drawdown: -4.58%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 23.05%\n",
      "Sortino: 9.71\n",
      "Max Drawdown: -1.67%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -1.30%\n",
      "Sortino: -0.58\n",
      "Max Drawdown: -4.46%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 3.18%\n",
      "Sortino: 0.84\n",
      "Max Drawdown: -5.37%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4154\n",
      "Total Return: -15.64%\n",
      "Sortino: -3.97\n",
      "Max Drawdown: -15.34%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 5.84%\n",
      "Sortino: 1.83\n",
      "Max Drawdown: -4.05%\n",
      "Number of Trades: 26\n",
      "\n",
      "Analyzing AUDUSD=X_2023\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 6.64%\n",
      "Sortino: 2.23\n",
      "Max Drawdown: -2.38%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -4.24%\n",
      "Sortino: -2.64\n",
      "Max Drawdown: -4.62%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8000\n",
      "Total Return: 28.77%\n",
      "Sortino: 18.26\n",
      "Max Drawdown: -1.06%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8254\n",
      "Total Return: 20.71%\n",
      "Sortino: 28.70\n",
      "Max Drawdown: -0.38%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 15.41%\n",
      "Sortino: 5.39\n",
      "Max Drawdown: -2.07%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 9.16%\n",
      "Sortino: 4.90\n",
      "Max Drawdown: -2.37%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 9.72%\n",
      "Sortino: 3.34\n",
      "Max Drawdown: -2.86%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 3.24%\n",
      "Sortino: 1.42\n",
      "Max Drawdown: -1.84%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 10.92%\n",
      "Sortino: 4.41\n",
      "Max Drawdown: -2.86%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 11.71%\n",
      "Sortino: 7.11\n",
      "Max Drawdown: -1.24%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7692\n",
      "Total Return: 20.23%\n",
      "Sortino: 7.44\n",
      "Max Drawdown: -1.88%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 18.63%\n",
      "Sortino: 20.80\n",
      "Max Drawdown: -0.41%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 6.71%\n",
      "Sortino: 2.20\n",
      "Max Drawdown: -3.78%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 2.61%\n",
      "Sortino: 0.99\n",
      "Max Drawdown: -2.43%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 12.15%\n",
      "Sortino: 4.91\n",
      "Max Drawdown: -2.86%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 7.99%\n",
      "Sortino: 4.67\n",
      "Max Drawdown: -2.17%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 23.95%\n",
      "Sortino: 15.06\n",
      "Max Drawdown: -1.06%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 11.47%\n",
      "Sortino: 8.45\n",
      "Max Drawdown: -0.77%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 9.24%\n",
      "Sortino: 3.29\n",
      "Max Drawdown: -2.32%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 3.10%\n",
      "Sortino: 1.24\n",
      "Max Drawdown: -2.39%\n",
      "Number of Trades: 11\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 4.10%\n",
      "Sortino: 1.19\n",
      "Max Drawdown: -2.86%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: -0.14%\n",
      "Sortino: -0.53\n",
      "Max Drawdown: -3.22%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 8.12%\n",
      "Sortino: 3.20\n",
      "Max Drawdown: -2.86%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 10.83%\n",
      "Sortino: 6.86\n",
      "Max Drawdown: -1.19%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 16.97%\n",
      "Sortino: 5.85\n",
      "Max Drawdown: -3.02%\n",
      "Number of Trades: 40\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 9.73%\n",
      "Sortino: 5.68\n",
      "Max Drawdown: -1.55%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 6.28%\n",
      "Sortino: 2.04\n",
      "Max Drawdown: -4.16%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 1.60%\n",
      "Sortino: 0.39\n",
      "Max Drawdown: -2.38%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -1.77%\n",
      "Sortino: -1.06\n",
      "Max Drawdown: -7.96%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 6.09%\n",
      "Sortino: 3.30\n",
      "Max Drawdown: -2.17%\n",
      "Number of Trades: 42\n",
      "\n",
      "Analyzing AUDUSD=X_2018-19\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -1.10%\n",
      "Sortino: -1.00\n",
      "Max Drawdown: -5.08%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -2.25%\n",
      "Sortino: -0.49\n",
      "Max Drawdown: -17.78%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8000\n",
      "Total Return: 34.63%\n",
      "Sortino: 13.45\n",
      "Max Drawdown: -1.24%\n",
      "Number of Trades: 66\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6719\n",
      "Total Return: 57.13%\n",
      "Sortino: 7.20\n",
      "Max Drawdown: -8.47%\n",
      "Number of Trades: 51\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 2.07%\n",
      "Sortino: 0.05\n",
      "Max Drawdown: -4.28%\n",
      "Number of Trades: 41\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5156\n",
      "Total Return: 11.10%\n",
      "Sortino: 1.22\n",
      "Max Drawdown: -10.00%\n",
      "Number of Trades: 38\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 3.49%\n",
      "Sortino: 0.52\n",
      "Max Drawdown: -3.51%\n",
      "Number of Trades: 9\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4844\n",
      "Total Return: 4.50%\n",
      "Sortino: 0.41\n",
      "Max Drawdown: -16.86%\n",
      "Number of Trades: 10\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 3.15%\n",
      "Sortino: 0.41\n",
      "Max Drawdown: -3.18%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4922\n",
      "Total Return: 4.75%\n",
      "Sortino: 0.43\n",
      "Max Drawdown: -16.23%\n",
      "Number of Trades: 14\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8154\n",
      "Total Return: 34.23%\n",
      "Sortino: 12.81\n",
      "Max Drawdown: -1.68%\n",
      "Number of Trades: 66\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6797\n",
      "Total Return: 55.73%\n",
      "Sortino: 6.64\n",
      "Max Drawdown: -8.47%\n",
      "Number of Trades: 55\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4538\n",
      "Total Return: 0.41%\n",
      "Sortino: -0.50\n",
      "Max Drawdown: -4.48%\n",
      "Number of Trades: 10\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5234\n",
      "Total Return: 23.66%\n",
      "Sortino: 3.07\n",
      "Max Drawdown: -9.22%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4846\n",
      "Total Return: 5.29%\n",
      "Sortino: 1.17\n",
      "Max Drawdown: -2.97%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4922\n",
      "Total Return: 3.79%\n",
      "Sortino: 0.31\n",
      "Max Drawdown: -16.82%\n",
      "Number of Trades: 10\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -0.62%\n",
      "Sortino: -0.82\n",
      "Max Drawdown: -4.76%\n",
      "Number of Trades: 9\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4531\n",
      "Total Return: -3.01%\n",
      "Sortino: -0.59\n",
      "Max Drawdown: -17.78%\n",
      "Number of Trades: 3\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5154\n",
      "Total Return: -3.13%\n",
      "Sortino: -1.50\n",
      "Max Drawdown: -4.39%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5156\n",
      "Total Return: 0.03%\n",
      "Sortino: -0.17\n",
      "Max Drawdown: -15.87%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4385\n",
      "Total Return: -4.28%\n",
      "Sortino: -1.95\n",
      "Max Drawdown: -6.91%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4766\n",
      "Total Return: -1.29%\n",
      "Sortino: -0.35\n",
      "Max Drawdown: -17.70%\n",
      "Number of Trades: 4\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 4.24%\n",
      "Sortino: 0.77\n",
      "Max Drawdown: -3.48%\n",
      "Number of Trades: 55\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 0.01%\n",
      "Sortino: -0.17\n",
      "Max Drawdown: -14.01%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 23.58%\n",
      "Sortino: 7.59\n",
      "Max Drawdown: -0.85%\n",
      "Number of Trades: 53\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5859\n",
      "Total Return: 23.56%\n",
      "Sortino: 2.84\n",
      "Max Drawdown: -8.47%\n",
      "Number of Trades: 39\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: 0.28%\n",
      "Sortino: -0.55\n",
      "Max Drawdown: -5.08%\n",
      "Number of Trades: 8\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4766\n",
      "Total Return: -0.00%\n",
      "Sortino: -0.18\n",
      "Max Drawdown: -16.86%\n",
      "Number of Trades: 3\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 0.84%\n",
      "Sortino: -0.34\n",
      "Max Drawdown: -2.54%\n",
      "Number of Trades: 61\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4688\n",
      "Total Return: -6.82%\n",
      "Sortino: -1.09\n",
      "Max Drawdown: -19.05%\n",
      "Number of Trades: 31\n",
      "\n",
      "Analyzing AUDUSD=X_2020-21\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (522, 6)\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (522, 9)\n",
      "Enhanced features shape before cleaning: (522, 9)\n",
      "Features shape after cleaning: (521, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -3.52%\n",
      "Sortino: -1.30\n",
      "Max Drawdown: -6.96%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -4.41%\n",
      "Sortino: -1.11\n",
      "Max Drawdown: -9.41%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 35.28%\n",
      "Sortino: 9.50\n",
      "Max Drawdown: -1.24%\n",
      "Number of Trades: 49\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7578\n",
      "Total Return: 67.97%\n",
      "Sortino: 17.04\n",
      "Max Drawdown: -1.18%\n",
      "Number of Trades: 46\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 8.29%\n",
      "Sortino: 1.44\n",
      "Max Drawdown: -4.31%\n",
      "Number of Trades: 74\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5938\n",
      "Total Return: 18.71%\n",
      "Sortino: 2.81\n",
      "Max Drawdown: -3.88%\n",
      "Number of Trades: 52\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 6.86%\n",
      "Sortino: 1.20\n",
      "Max Drawdown: -4.76%\n",
      "Number of Trades: 64\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5703\n",
      "Total Return: 3.29%\n",
      "Sortino: 0.26\n",
      "Max Drawdown: -5.53%\n",
      "Number of Trades: 62\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 0.28%\n",
      "Sortino: -0.40\n",
      "Max Drawdown: -5.79%\n",
      "Number of Trades: 60\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5391\n",
      "Total Return: -0.27%\n",
      "Sortino: -0.31\n",
      "Max Drawdown: -9.51%\n",
      "Number of Trades: 54\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8077\n",
      "Total Return: 47.52%\n",
      "Sortino: 15.33\n",
      "Max Drawdown: -0.62%\n",
      "Number of Trades: 75\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7891\n",
      "Total Return: 71.54%\n",
      "Sortino: 18.08\n",
      "Max Drawdown: -1.11%\n",
      "Number of Trades: 60\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 1.19%\n",
      "Sortino: -0.17\n",
      "Max Drawdown: -5.99%\n",
      "Number of Trades: 63\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5703\n",
      "Total Return: 4.49%\n",
      "Sortino: 0.46\n",
      "Max Drawdown: -7.90%\n",
      "Number of Trades: 48\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 6.26%\n",
      "Sortino: 1.09\n",
      "Max Drawdown: -5.58%\n",
      "Number of Trades: 64\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6406\n",
      "Total Return: 21.39%\n",
      "Sortino: 3.29\n",
      "Max Drawdown: -4.01%\n",
      "Number of Trades: 54\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: 8.66%\n",
      "Sortino: 1.63\n",
      "Max Drawdown: -3.41%\n",
      "Number of Trades: 54\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6250\n",
      "Total Return: 17.96%\n",
      "Sortino: 2.51\n",
      "Max Drawdown: -3.33%\n",
      "Number of Trades: 49\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 4.28%\n",
      "Sortino: 0.59\n",
      "Max Drawdown: -4.50%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5859\n",
      "Total Return: 3.30%\n",
      "Sortino: 0.25\n",
      "Max Drawdown: -8.14%\n",
      "Number of Trades: 63\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 5.49%\n",
      "Sortino: 0.91\n",
      "Max Drawdown: -3.42%\n",
      "Number of Trades: 58\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: 7.60%\n",
      "Sortino: 0.98\n",
      "Max Drawdown: -5.90%\n",
      "Number of Trades: 54\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 10.91%\n",
      "Sortino: 2.39\n",
      "Max Drawdown: -3.61%\n",
      "Number of Trades: 56\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6328\n",
      "Total Return: 27.79%\n",
      "Sortino: 4.62\n",
      "Max Drawdown: -2.67%\n",
      "Number of Trades: 52\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7769\n",
      "Total Return: 42.51%\n",
      "Sortino: 12.48\n",
      "Max Drawdown: -0.85%\n",
      "Number of Trades: 70\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7969\n",
      "Total Return: 72.83%\n",
      "Sortino: 17.98\n",
      "Max Drawdown: -1.05%\n",
      "Number of Trades: 58\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -3.40%\n",
      "Sortino: -1.25\n",
      "Max Drawdown: -5.63%\n",
      "Number of Trades: 7\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5234\n",
      "Total Return: 4.36%\n",
      "Sortino: 0.45\n",
      "Max Drawdown: -6.35%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.09%\n",
      "Sortino: -0.48\n",
      "Max Drawdown: -4.77%\n",
      "Number of Trades: 48\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5625\n",
      "Total Return: 1.18%\n",
      "Sortino: -0.07\n",
      "Max Drawdown: -12.04%\n",
      "Number of Trades: 44\n",
      "\n",
      "Analyzing AUDUSD=X_2022-23\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 3.55%\n",
      "Sortino: 0.34\n",
      "Max Drawdown: -8.59%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -2.43%\n",
      "Sortino: -0.94\n",
      "Max Drawdown: -5.22%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7692\n",
      "Total Return: 50.90%\n",
      "Sortino: 11.56\n",
      "Max Drawdown: -1.80%\n",
      "Number of Trades: 55\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7578\n",
      "Total Return: 45.17%\n",
      "Sortino: 17.54\n",
      "Max Drawdown: -0.76%\n",
      "Number of Trades: 54\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 4.36%\n",
      "Sortino: 0.51\n",
      "Max Drawdown: -5.00%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6250\n",
      "Total Return: 20.13%\n",
      "Sortino: 4.84\n",
      "Max Drawdown: -2.50%\n",
      "Number of Trades: 58\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 10.97%\n",
      "Sortino: 1.79\n",
      "Max Drawdown: -4.14%\n",
      "Number of Trades: 50\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6094\n",
      "Total Return: 18.50%\n",
      "Sortino: 4.52\n",
      "Max Drawdown: -2.25%\n",
      "Number of Trades: 57\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 14.23%\n",
      "Sortino: 2.57\n",
      "Max Drawdown: -8.32%\n",
      "Number of Trades: 60\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6016\n",
      "Total Return: 11.79%\n",
      "Sortino: 2.46\n",
      "Max Drawdown: -2.36%\n",
      "Number of Trades: 46\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8231\n",
      "Total Return: 56.85%\n",
      "Sortino: 12.96\n",
      "Max Drawdown: -1.98%\n",
      "Number of Trades: 67\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8203\n",
      "Total Return: 51.61%\n",
      "Sortino: 25.69\n",
      "Max Drawdown: -0.41%\n",
      "Number of Trades: 66\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: 9.00%\n",
      "Sortino: 1.40\n",
      "Max Drawdown: -8.48%\n",
      "Number of Trades: 44\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6328\n",
      "Total Return: 11.46%\n",
      "Sortino: 2.07\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 38\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: 7.06%\n",
      "Sortino: 1.00\n",
      "Max Drawdown: -9.53%\n",
      "Number of Trades: 53\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5859\n",
      "Total Return: 10.63%\n",
      "Sortino: 2.14\n",
      "Max Drawdown: -4.19%\n",
      "Number of Trades: 55\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 20.36%\n",
      "Sortino: 3.80\n",
      "Max Drawdown: -2.61%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: 8.52%\n",
      "Sortino: 1.63\n",
      "Max Drawdown: -2.43%\n",
      "Number of Trades: 10\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5308\n",
      "Total Return: 12.95%\n",
      "Sortino: 2.38\n",
      "Max Drawdown: -4.46%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 4.84%\n",
      "Sortino: 0.69\n",
      "Max Drawdown: -5.27%\n",
      "Number of Trades: 49\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: 1.80%\n",
      "Sortino: 0.01\n",
      "Max Drawdown: -7.80%\n",
      "Number of Trades: 70\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5781\n",
      "Total Return: 7.66%\n",
      "Sortino: 1.32\n",
      "Max Drawdown: -4.56%\n",
      "Number of Trades: 55\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5923\n",
      "Total Return: 24.52%\n",
      "Sortino: 5.21\n",
      "Max Drawdown: -5.25%\n",
      "Number of Trades: 51\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6328\n",
      "Total Return: 22.97%\n",
      "Sortino: 5.95\n",
      "Max Drawdown: -1.74%\n",
      "Number of Trades: 61\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7846\n",
      "Total Return: 52.32%\n",
      "Sortino: 12.64\n",
      "Max Drawdown: -1.37%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7969\n",
      "Total Return: 47.46%\n",
      "Sortino: 19.88\n",
      "Max Drawdown: -0.60%\n",
      "Number of Trades: 64\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -2.53%\n",
      "Sortino: -0.84\n",
      "Max Drawdown: -10.37%\n",
      "Number of Trades: 53\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5391\n",
      "Total Return: 3.92%\n",
      "Sortino: 0.53\n",
      "Max Drawdown: -3.54%\n",
      "Number of Trades: 34\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -2.56%\n",
      "Sortino: -0.83\n",
      "Max Drawdown: -8.31%\n",
      "Number of Trades: 74\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4844\n",
      "Total Return: -2.34%\n",
      "Sortino: -1.06\n",
      "Max Drawdown: -6.12%\n",
      "Number of Trades: 66\n",
      "\n",
      "Analyzing AUDUSD=X_2018-23\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (1564, 6)\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (270, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (1564, 9)\n",
      "Enhanced features shape before cleaning: (1564, 9)\n",
      "Features shape after cleaning: (1563, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for AUDUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -0.37%\n",
      "Sortino: -0.28\n",
      "Max Drawdown: -12.54%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -9.12%\n",
      "Sortino: -1.37\n",
      "Max Drawdown: -10.54%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8005\n",
      "Total Return: 439.73%\n",
      "Sortino: 14.25\n",
      "Max Drawdown: -2.03%\n",
      "Number of Trades: 186\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7948\n",
      "Total Return: 126.88%\n",
      "Sortino: 16.99\n",
      "Max Drawdown: -1.55%\n",
      "Number of Trades: 121\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5780\n",
      "Total Return: 17.45%\n",
      "Sortino: 0.55\n",
      "Max Drawdown: -15.29%\n",
      "Number of Trades: 193\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5037\n",
      "Total Return: -13.41%\n",
      "Sortino: -1.81\n",
      "Max Drawdown: -16.17%\n",
      "Number of Trades: 124\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5601\n",
      "Total Return: 39.84%\n",
      "Sortino: 1.59\n",
      "Max Drawdown: -7.49%\n",
      "Number of Trades: 184\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5560\n",
      "Total Return: 7.71%\n",
      "Sortino: 0.40\n",
      "Max Drawdown: -4.33%\n",
      "Number of Trades: 124\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5652\n",
      "Total Return: 27.24%\n",
      "Sortino: 0.98\n",
      "Max Drawdown: -7.73%\n",
      "Number of Trades: 175\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5522\n",
      "Total Return: 13.27%\n",
      "Sortino: 0.97\n",
      "Max Drawdown: -8.34%\n",
      "Number of Trades: 120\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7775\n",
      "Total Return: 406.10%\n",
      "Sortino: 13.62\n",
      "Max Drawdown: -2.03%\n",
      "Number of Trades: 178\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7910\n",
      "Total Return: 126.90%\n",
      "Sortino: 18.25\n",
      "Max Drawdown: -1.55%\n",
      "Number of Trades: 121\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5345\n",
      "Total Return: -3.61%\n",
      "Sortino: -0.43\n",
      "Max Drawdown: -20.72%\n",
      "Number of Trades: 141\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5448\n",
      "Total Return: 17.06%\n",
      "Sortino: 1.44\n",
      "Max Drawdown: -8.55%\n",
      "Number of Trades: 98\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5934\n",
      "Total Return: 68.36%\n",
      "Sortino: 2.70\n",
      "Max Drawdown: -9.81%\n",
      "Number of Trades: 170\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5709\n",
      "Total Return: 18.67%\n",
      "Sortino: 1.54\n",
      "Max Drawdown: -4.60%\n",
      "Number of Trades: 110\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7008\n",
      "Total Return: 241.71%\n",
      "Sortino: 7.81\n",
      "Max Drawdown: -3.20%\n",
      "Number of Trades: 156\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6679\n",
      "Total Return: 80.30%\n",
      "Sortino: 9.25\n",
      "Max Drawdown: -1.55%\n",
      "Number of Trades: 107\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4706\n",
      "Total Return: -36.03%\n",
      "Sortino: -2.39\n",
      "Max Drawdown: -35.80%\n",
      "Number of Trades: 143\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4888\n",
      "Total Return: -5.05%\n",
      "Sortino: -0.97\n",
      "Max Drawdown: -14.89%\n",
      "Number of Trades: 51\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5141\n",
      "Total Return: 13.65%\n",
      "Sortino: 0.44\n",
      "Max Drawdown: -7.06%\n",
      "Number of Trades: 177\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5075\n",
      "Total Return: -9.81%\n",
      "Sortino: -1.49\n",
      "Max Drawdown: -11.11%\n",
      "Number of Trades: 112\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6471\n",
      "Total Return: 154.31%\n",
      "Sortino: 5.77\n",
      "Max Drawdown: -4.92%\n",
      "Number of Trades: 182\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5784\n",
      "Total Return: 24.14%\n",
      "Sortino: 2.18\n",
      "Max Drawdown: -4.62%\n",
      "Number of Trades: 119\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7826\n",
      "Total Return: 393.75%\n",
      "Sortino: 12.23\n",
      "Max Drawdown: -2.29%\n",
      "Number of Trades: 180\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8060\n",
      "Total Return: 132.87%\n",
      "Sortino: 22.25\n",
      "Max Drawdown: -0.61%\n",
      "Number of Trades: 133\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5294\n",
      "Total Return: -4.05%\n",
      "Sortino: -0.47\n",
      "Max Drawdown: -16.06%\n",
      "Number of Trades: 184\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4888\n",
      "Total Return: -14.01%\n",
      "Sortino: -1.95\n",
      "Max Drawdown: -18.40%\n",
      "Number of Trades: 122\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5115\n",
      "Total Return: -12.53%\n",
      "Sortino: -0.92\n",
      "Max Drawdown: -13.30%\n",
      "Number of Trades: 211\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4963\n",
      "Total Return: -3.99%\n",
      "Sortino: -0.87\n",
      "Max Drawdown: -14.98%\n",
      "Number of Trades: 129\n",
      "\n",
      "Analyzing MXNUSD=X_2018\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -5.56%\n",
      "Sortino: -1.58\n",
      "Max Drawdown: -9.32%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 1.46%\n",
      "Sortino: 0.24\n",
      "Max Drawdown: -3.44%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 23.58%\n",
      "Sortino: 7.64\n",
      "Max Drawdown: -2.27%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 17.64%\n",
      "Sortino: 15.06\n",
      "Max Drawdown: -0.89%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 9.36%\n",
      "Sortino: 2.69\n",
      "Max Drawdown: -3.60%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 2.51%\n",
      "Sortino: 0.88\n",
      "Max Drawdown: -3.82%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 11.97%\n",
      "Sortino: 3.63\n",
      "Max Drawdown: -3.52%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 5.34%\n",
      "Sortino: 2.73\n",
      "Max Drawdown: -2.14%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 20.05%\n",
      "Sortino: 6.27\n",
      "Max Drawdown: -2.23%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 5.39%\n",
      "Sortino: 2.54\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 27.09%\n",
      "Sortino: 11.00\n",
      "Max Drawdown: -2.06%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 17.98%\n",
      "Sortino: 15.04\n",
      "Max Drawdown: -0.73%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 8.58%\n",
      "Sortino: 2.50\n",
      "Max Drawdown: -2.72%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 0.31%\n",
      "Sortino: -0.30\n",
      "Max Drawdown: -5.08%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 8.94%\n",
      "Sortino: 2.58\n",
      "Max Drawdown: -5.67%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 6.96%\n",
      "Sortino: 3.60\n",
      "Max Drawdown: -2.20%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 11.26%\n",
      "Sortino: 3.20\n",
      "Max Drawdown: -4.31%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 12.94%\n",
      "Sortino: 7.83\n",
      "Max Drawdown: -2.21%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -1.35%\n",
      "Sortino: -0.63\n",
      "Max Drawdown: -6.17%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: -8.65%\n",
      "Sortino: -4.27\n",
      "Max Drawdown: -10.06%\n",
      "Number of Trades: 36\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 1.16%\n",
      "Sortino: 0.12\n",
      "Max Drawdown: -4.86%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -4.38%\n",
      "Sortino: -2.33\n",
      "Max Drawdown: -7.85%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 11.70%\n",
      "Sortino: 3.27\n",
      "Max Drawdown: -2.33%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 10.01%\n",
      "Sortino: 5.40\n",
      "Max Drawdown: -2.05%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 21.37%\n",
      "Sortino: 7.55\n",
      "Max Drawdown: -3.27%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 17.73%\n",
      "Sortino: 17.51\n",
      "Max Drawdown: -0.54%\n",
      "Number of Trades: 38\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 0.04%\n",
      "Sortino: -0.21\n",
      "Max Drawdown: -5.82%\n",
      "Number of Trades: 7\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -4.38%\n",
      "Sortino: -2.32\n",
      "Max Drawdown: -7.40%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: -2.42%\n",
      "Sortino: -0.77\n",
      "Max Drawdown: -8.94%\n",
      "Number of Trades: 3\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 1.41%\n",
      "Sortino: 0.24\n",
      "Max Drawdown: -3.44%\n",
      "Number of Trades: 1\n",
      "\n",
      "Analyzing MXNUSD=X_2019\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (259, 6)\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (259, 9)\n",
      "Enhanced features shape before cleaning: (259, 9)\n",
      "Features shape after cleaning: (258, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 3.96%\n",
      "Sortino: 1.90\n",
      "Max Drawdown: -2.73%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -19.90%\n",
      "Sortino: -3.34\n",
      "Max Drawdown: -26.81%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 14.47%\n",
      "Sortino: 15.36\n",
      "Max Drawdown: -0.94%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 52.65%\n",
      "Sortino: 10.80\n",
      "Max Drawdown: -5.09%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 1.97%\n",
      "Sortino: 0.62\n",
      "Max Drawdown: -2.81%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -3.22%\n",
      "Sortino: -0.53\n",
      "Max Drawdown: -10.38%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 3.75%\n",
      "Sortino: 1.70\n",
      "Max Drawdown: -2.06%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: -14.45%\n",
      "Sortino: -2.12\n",
      "Max Drawdown: -20.71%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -0.48%\n",
      "Sortino: -0.87\n",
      "Max Drawdown: -4.12%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -4.30%\n",
      "Sortino: -0.70\n",
      "Max Drawdown: -10.38%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 11.50%\n",
      "Sortino: 9.67\n",
      "Max Drawdown: -0.94%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8413\n",
      "Total Return: 52.82%\n",
      "Sortino: 9.56\n",
      "Max Drawdown: -5.09%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -1.01%\n",
      "Sortino: -1.15\n",
      "Max Drawdown: -4.12%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 3.98%\n",
      "Sortino: 0.64\n",
      "Max Drawdown: -17.95%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 2.65%\n",
      "Sortino: 0.97\n",
      "Max Drawdown: -3.14%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -14.05%\n",
      "Sortino: -2.18\n",
      "Max Drawdown: -20.88%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 10.65%\n",
      "Sortino: 9.46\n",
      "Max Drawdown: -0.66%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 27.63%\n",
      "Sortino: 5.08\n",
      "Max Drawdown: -10.80%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 0.30%\n",
      "Sortino: -0.40\n",
      "Max Drawdown: -3.53%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3968\n",
      "Total Return: -16.46%\n",
      "Sortino: -2.81\n",
      "Max Drawdown: -22.35%\n",
      "Number of Trades: 10\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -0.70%\n",
      "Sortino: -1.00\n",
      "Max Drawdown: -5.13%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -13.03%\n",
      "Sortino: -2.16\n",
      "Max Drawdown: -15.22%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 3.02%\n",
      "Sortino: 1.36\n",
      "Max Drawdown: -3.03%\n",
      "Number of Trades: 40\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 18.17%\n",
      "Sortino: 3.28\n",
      "Max Drawdown: -8.53%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 12.53%\n",
      "Sortino: 9.27\n",
      "Max Drawdown: -0.83%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 41.46%\n",
      "Sortino: 6.70\n",
      "Max Drawdown: -5.09%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 3.92%\n",
      "Sortino: 1.66\n",
      "Max Drawdown: -1.93%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 18.15%\n",
      "Sortino: 3.80\n",
      "Max Drawdown: -8.53%\n",
      "Number of Trades: 11\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 4.72%\n",
      "Sortino: 2.38\n",
      "Max Drawdown: -2.40%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3968\n",
      "Total Return: -21.43%\n",
      "Sortino: -3.71\n",
      "Max Drawdown: -27.78%\n",
      "Number of Trades: 13\n",
      "\n",
      "Analyzing MXNUSD=X_2020\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (261, 6)\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (261, 9)\n",
      "Enhanced features shape before cleaning: (261, 9)\n",
      "Features shape after cleaning: (260, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 11.83%\n",
      "Sortino: 4.02\n",
      "Max Drawdown: -2.24%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -3.54%\n",
      "Sortino: -1.06\n",
      "Max Drawdown: -8.88%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7538\n",
      "Total Return: 26.83%\n",
      "Sortino: 9.23\n",
      "Max Drawdown: -1.63%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7619\n",
      "Total Return: 30.64%\n",
      "Sortino: 10.21\n",
      "Max Drawdown: -1.48%\n",
      "Number of Trades: 37\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 0.49%\n",
      "Sortino: -0.10\n",
      "Max Drawdown: -4.14%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 11.15%\n",
      "Sortino: 3.27\n",
      "Max Drawdown: -2.83%\n",
      "Number of Trades: 34\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 2.41%\n",
      "Sortino: 0.48\n",
      "Max Drawdown: -6.53%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 7.40%\n",
      "Sortino: 2.17\n",
      "Max Drawdown: -2.82%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 7.24%\n",
      "Sortino: 1.99\n",
      "Max Drawdown: -4.14%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 9.84%\n",
      "Sortino: 2.85\n",
      "Max Drawdown: -3.01%\n",
      "Number of Trades: 37\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 27.36%\n",
      "Sortino: 11.36\n",
      "Max Drawdown: -2.52%\n",
      "Number of Trades: 40\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7619\n",
      "Total Return: 32.92%\n",
      "Sortino: 11.64\n",
      "Max Drawdown: -1.94%\n",
      "Number of Trades: 36\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 1.57%\n",
      "Sortino: 0.25\n",
      "Max Drawdown: -4.97%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 12.98%\n",
      "Sortino: 3.89\n",
      "Max Drawdown: -2.83%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 3.35%\n",
      "Sortino: 0.83\n",
      "Max Drawdown: -3.24%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 3.76%\n",
      "Sortino: 0.94\n",
      "Max Drawdown: -3.52%\n",
      "Number of Trades: 44\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -2.23%\n",
      "Sortino: -0.97\n",
      "Max Drawdown: -7.33%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 13.30%\n",
      "Sortino: 3.66\n",
      "Max Drawdown: -2.83%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -5.63%\n",
      "Sortino: -2.12\n",
      "Max Drawdown: -7.71%\n",
      "Number of Trades: 17\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: 6.11%\n",
      "Sortino: 1.73\n",
      "Max Drawdown: -5.33%\n",
      "Number of Trades: 5\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -7.69%\n",
      "Sortino: -2.72\n",
      "Max Drawdown: -7.57%\n",
      "Number of Trades: 5\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: 3.10%\n",
      "Sortino: 0.78\n",
      "Max Drawdown: -5.33%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -0.74%\n",
      "Sortino: -0.51\n",
      "Max Drawdown: -4.42%\n",
      "Number of Trades: 39\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 8.45%\n",
      "Sortino: 2.52\n",
      "Max Drawdown: -3.77%\n",
      "Number of Trades: 38\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7538\n",
      "Total Return: 25.15%\n",
      "Sortino: 8.48\n",
      "Max Drawdown: -1.63%\n",
      "Number of Trades: 41\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7619\n",
      "Total Return: 33.74%\n",
      "Sortino: 12.17\n",
      "Max Drawdown: -1.48%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -2.19%\n",
      "Sortino: -0.95\n",
      "Max Drawdown: -4.97%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 9.28%\n",
      "Sortino: 2.65\n",
      "Max Drawdown: -2.84%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -0.83%\n",
      "Sortino: -0.53\n",
      "Max Drawdown: -7.36%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 10.02%\n",
      "Sortino: 2.57\n",
      "Max Drawdown: -4.39%\n",
      "Number of Trades: 22\n",
      "\n",
      "Analyzing MXNUSD=X_2021\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -0.78%\n",
      "Sortino: -0.65\n",
      "Max Drawdown: -7.18%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 3.06%\n",
      "Sortino: 0.91\n",
      "Max Drawdown: -5.29%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 17.51%\n",
      "Sortino: 8.50\n",
      "Max Drawdown: -1.88%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 16.82%\n",
      "Sortino: 12.63\n",
      "Max Drawdown: -1.23%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -2.70%\n",
      "Sortino: -1.30\n",
      "Max Drawdown: -5.58%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 4.60%\n",
      "Sortino: 1.40\n",
      "Max Drawdown: -4.60%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 7.60%\n",
      "Sortino: 2.99\n",
      "Max Drawdown: -2.90%\n",
      "Number of Trades: 17\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 7.83%\n",
      "Sortino: 3.05\n",
      "Max Drawdown: -2.27%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 5.70%\n",
      "Sortino: 1.91\n",
      "Max Drawdown: -2.28%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 6.75%\n",
      "Sortino: 2.30\n",
      "Max Drawdown: -4.11%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 20.32%\n",
      "Sortino: 10.07\n",
      "Max Drawdown: -1.71%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 17.13%\n",
      "Sortino: 12.88\n",
      "Max Drawdown: -1.07%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 2.15%\n",
      "Sortino: 0.50\n",
      "Max Drawdown: -5.91%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 5.15%\n",
      "Sortino: 1.83\n",
      "Max Drawdown: -4.11%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 7.77%\n",
      "Sortino: 3.08\n",
      "Max Drawdown: -2.69%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 7.06%\n",
      "Sortino: 2.45\n",
      "Max Drawdown: -3.60%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 3.08%\n",
      "Sortino: 0.93\n",
      "Max Drawdown: -5.14%\n",
      "Number of Trades: 7\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 10.49%\n",
      "Sortino: 5.30\n",
      "Max Drawdown: -1.84%\n",
      "Number of Trades: 11\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 0.32%\n",
      "Sortino: -0.20\n",
      "Max Drawdown: -5.91%\n",
      "Number of Trades: 11\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 4.48%\n",
      "Sortino: 1.49\n",
      "Max Drawdown: -4.11%\n",
      "Number of Trades: 11\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 2.30%\n",
      "Sortino: 0.55\n",
      "Max Drawdown: -5.91%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 4.75%\n",
      "Sortino: 1.52\n",
      "Max Drawdown: -4.11%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 13.50%\n",
      "Sortino: 5.97\n",
      "Max Drawdown: -3.68%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 5.00%\n",
      "Sortino: 1.66\n",
      "Max Drawdown: -4.22%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 12.29%\n",
      "Sortino: 5.02\n",
      "Max Drawdown: -2.05%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 6.33%\n",
      "Sortino: 2.95\n",
      "Max Drawdown: -2.05%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 0.03%\n",
      "Sortino: -0.32\n",
      "Max Drawdown: -5.91%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 3.66%\n",
      "Sortino: 1.20\n",
      "Max Drawdown: -4.11%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -3.54%\n",
      "Sortino: -1.71\n",
      "Max Drawdown: -7.18%\n",
      "Number of Trades: 5\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 6.59%\n",
      "Sortino: 2.57\n",
      "Max Drawdown: -3.53%\n",
      "Number of Trades: 10\n",
      "\n",
      "Analyzing MXNUSD=X_2022\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 3.62%\n",
      "Sortino: 1.09\n",
      "Max Drawdown: -3.62%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 7.54%\n",
      "Sortino: 1.50\n",
      "Max Drawdown: -5.38%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8462\n",
      "Total Return: 24.16%\n",
      "Sortino: 16.15\n",
      "Max Drawdown: -0.62%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 26.10%\n",
      "Sortino: 9.13\n",
      "Max Drawdown: -1.71%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 15.24%\n",
      "Sortino: 9.18\n",
      "Max Drawdown: -1.02%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 1.60%\n",
      "Sortino: 0.21\n",
      "Max Drawdown: -8.10%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 7.48%\n",
      "Sortino: 3.21\n",
      "Max Drawdown: -3.33%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3333\n",
      "Total Return: -7.48%\n",
      "Sortino: -3.13\n",
      "Max Drawdown: -7.58%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 11.69%\n",
      "Sortino: 5.43\n",
      "Max Drawdown: -1.70%\n",
      "Number of Trades: 17\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 9.19%\n",
      "Sortino: 1.83\n",
      "Max Drawdown: -6.91%\n",
      "Number of Trades: 9\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 16.53%\n",
      "Sortino: 8.76\n",
      "Max Drawdown: -1.52%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3333\n",
      "Total Return: -7.48%\n",
      "Sortino: -3.13\n",
      "Max Drawdown: -7.58%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 8.14%\n",
      "Sortino: 3.17\n",
      "Max Drawdown: -2.27%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3333\n",
      "Total Return: -7.48%\n",
      "Sortino: -3.13\n",
      "Max Drawdown: -7.58%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 4.86%\n",
      "Sortino: 1.89\n",
      "Max Drawdown: -3.53%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3968\n",
      "Total Return: -11.67%\n",
      "Sortino: -3.80\n",
      "Max Drawdown: -12.63%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 7.47%\n",
      "Sortino: 2.61\n",
      "Max Drawdown: -1.89%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3492\n",
      "Total Return: -6.81%\n",
      "Sortino: -2.80\n",
      "Max Drawdown: -7.25%\n",
      "Number of Trades: 5\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -0.11%\n",
      "Sortino: -0.42\n",
      "Max Drawdown: -3.63%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3175\n",
      "Total Return: -12.91%\n",
      "Sortino: -4.77\n",
      "Max Drawdown: -12.63%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -1.32%\n",
      "Sortino: -0.94\n",
      "Max Drawdown: -4.47%\n",
      "Number of Trades: 6\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3333\n",
      "Total Return: -7.48%\n",
      "Sortino: -3.13\n",
      "Max Drawdown: -7.58%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 6.50%\n",
      "Sortino: 2.41\n",
      "Max Drawdown: -4.54%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3016\n",
      "Total Return: -18.62%\n",
      "Sortino: -6.16\n",
      "Max Drawdown: -18.93%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 16.49%\n",
      "Sortino: 9.00\n",
      "Max Drawdown: -1.60%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3968\n",
      "Total Return: -4.45%\n",
      "Sortino: -1.90\n",
      "Max Drawdown: -6.32%\n",
      "Number of Trades: 9\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 1.57%\n",
      "Sortino: 0.28\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 8\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3333\n",
      "Total Return: -7.48%\n",
      "Sortino: -3.13\n",
      "Max Drawdown: -7.58%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 2.80%\n",
      "Sortino: 0.83\n",
      "Max Drawdown: -3.27%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 14.08%\n",
      "Sortino: 4.20\n",
      "Max Drawdown: -2.63%\n",
      "Number of Trades: 19\n",
      "\n",
      "Analyzing MXNUSD=X_2023\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 3.70%\n",
      "Sortino: 0.77\n",
      "Max Drawdown: -5.09%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 2.49%\n",
      "Sortino: 0.70\n",
      "Max Drawdown: -2.73%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7538\n",
      "Total Return: 24.35%\n",
      "Sortino: 8.59\n",
      "Max Drawdown: -3.45%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 13.65%\n",
      "Sortino: 9.24\n",
      "Max Drawdown: -0.77%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -4.29%\n",
      "Sortino: -1.57\n",
      "Max Drawdown: -7.57%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 6.83%\n",
      "Sortino: 3.94\n",
      "Max Drawdown: -1.42%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 6.93%\n",
      "Sortino: 2.01\n",
      "Max Drawdown: -2.87%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7619\n",
      "Total Return: 12.99%\n",
      "Sortino: 8.72\n",
      "Max Drawdown: -0.87%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 12.78%\n",
      "Sortino: 4.30\n",
      "Max Drawdown: -1.82%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7143\n",
      "Total Return: 10.55%\n",
      "Sortino: 6.28\n",
      "Max Drawdown: -1.24%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 22.00%\n",
      "Sortino: 6.99\n",
      "Max Drawdown: -1.93%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 15.63%\n",
      "Sortino: 15.79\n",
      "Max Drawdown: -0.66%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 11.80%\n",
      "Sortino: 3.59\n",
      "Max Drawdown: -3.89%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 10.73%\n",
      "Sortino: 5.95\n",
      "Max Drawdown: -1.46%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 7.85%\n",
      "Sortino: 2.15\n",
      "Max Drawdown: -3.25%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 11.11%\n",
      "Sortino: 6.88\n",
      "Max Drawdown: -1.42%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 6.91%\n",
      "Sortino: 2.10\n",
      "Max Drawdown: -4.65%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 2.65%\n",
      "Sortino: 1.16\n",
      "Max Drawdown: -2.41%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 2.02%\n",
      "Sortino: 0.33\n",
      "Max Drawdown: -6.46%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 5.78%\n",
      "Sortino: 3.24\n",
      "Max Drawdown: -1.30%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 9.32%\n",
      "Sortino: 3.02\n",
      "Max Drawdown: -4.64%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 8.92%\n",
      "Sortino: 5.16\n",
      "Max Drawdown: -1.24%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 19.03%\n",
      "Sortino: 5.71\n",
      "Max Drawdown: -2.11%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 11.66%\n",
      "Sortino: 7.59\n",
      "Max Drawdown: -1.24%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8154\n",
      "Total Return: 27.01%\n",
      "Sortino: 8.93\n",
      "Max Drawdown: -1.93%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 7.84%\n",
      "Sortino: 4.52\n",
      "Max Drawdown: -1.78%\n",
      "Number of Trades: 34\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 3.42%\n",
      "Sortino: 0.83\n",
      "Max Drawdown: -6.77%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 4.90%\n",
      "Sortino: 2.18\n",
      "Max Drawdown: -1.97%\n",
      "Number of Trades: 14\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 8.99%\n",
      "Sortino: 2.51\n",
      "Max Drawdown: -5.38%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 6.19%\n",
      "Sortino: 3.32\n",
      "Max Drawdown: -1.30%\n",
      "Number of Trades: 15\n",
      "\n",
      "Analyzing MXNUSD=X_2018-19\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 1.10%\n",
      "Sortino: -0.18\n",
      "Max Drawdown: -5.99%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -18.03%\n",
      "Sortino: -1.47\n",
      "Max Drawdown: -26.81%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6538\n",
      "Total Return: 25.57%\n",
      "Sortino: 6.92\n",
      "Max Drawdown: -2.32%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7656\n",
      "Total Return: 126.26%\n",
      "Sortino: 8.80\n",
      "Max Drawdown: -5.09%\n",
      "Number of Trades: 41\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6231\n",
      "Total Return: 15.49%\n",
      "Sortino: 3.46\n",
      "Max Drawdown: -1.66%\n",
      "Number of Trades: 36\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5234\n",
      "Total Return: 18.37%\n",
      "Sortino: 1.55\n",
      "Max Drawdown: -14.62%\n",
      "Number of Trades: 71\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6231\n",
      "Total Return: 18.85%\n",
      "Sortino: 4.75\n",
      "Max Drawdown: -2.28%\n",
      "Number of Trades: 38\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5781\n",
      "Total Return: 5.82%\n",
      "Sortino: 0.39\n",
      "Max Drawdown: -17.95%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6385\n",
      "Total Return: 21.47%\n",
      "Sortino: 5.04\n",
      "Max Drawdown: -2.07%\n",
      "Number of Trades: 44\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5859\n",
      "Total Return: 3.22%\n",
      "Sortino: 0.20\n",
      "Max Drawdown: -17.88%\n",
      "Number of Trades: 38\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 27.59%\n",
      "Sortino: 8.67\n",
      "Max Drawdown: -2.32%\n",
      "Number of Trades: 46\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7109\n",
      "Total Return: 108.13%\n",
      "Sortino: 6.76\n",
      "Max Drawdown: -9.16%\n",
      "Number of Trades: 57\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5923\n",
      "Total Return: 11.12%\n",
      "Sortino: 2.17\n",
      "Max Drawdown: -2.49%\n",
      "Number of Trades: 36\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5156\n",
      "Total Return: -18.82%\n",
      "Sortino: -1.61\n",
      "Max Drawdown: -28.32%\n",
      "Number of Trades: 54\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 19.67%\n",
      "Sortino: 4.73\n",
      "Max Drawdown: -1.98%\n",
      "Number of Trades: 50\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 3.41%\n",
      "Sortino: 0.23\n",
      "Max Drawdown: -21.10%\n",
      "Number of Trades: 52\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6077\n",
      "Total Return: 19.57%\n",
      "Sortino: 5.02\n",
      "Max Drawdown: -2.32%\n",
      "Number of Trades: 17\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6953\n",
      "Total Return: 62.91%\n",
      "Sortino: 4.10\n",
      "Max Drawdown: -6.50%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 7.47%\n",
      "Sortino: 1.37\n",
      "Max Drawdown: -5.61%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5391\n",
      "Total Return: 20.10%\n",
      "Sortino: 1.66\n",
      "Max Drawdown: -13.81%\n",
      "Number of Trades: 36\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 15.04%\n",
      "Sortino: 3.25\n",
      "Max Drawdown: -3.57%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5391\n",
      "Total Return: 2.79%\n",
      "Sortino: 0.18\n",
      "Max Drawdown: -22.39%\n",
      "Number of Trades: 54\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6538\n",
      "Total Return: 21.49%\n",
      "Sortino: 5.26\n",
      "Max Drawdown: -1.98%\n",
      "Number of Trades: 46\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 0.08%\n",
      "Sortino: -0.03\n",
      "Max Drawdown: -21.38%\n",
      "Number of Trades: 58\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 18.44%\n",
      "Sortino: 4.17\n",
      "Max Drawdown: -2.32%\n",
      "Number of Trades: 43\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6953\n",
      "Total Return: 106.92%\n",
      "Sortino: 7.42\n",
      "Max Drawdown: -5.09%\n",
      "Number of Trades: 50\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 1.14%\n",
      "Sortino: -0.16\n",
      "Max Drawdown: -3.29%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5156\n",
      "Total Return: -6.98%\n",
      "Sortino: -0.61\n",
      "Max Drawdown: -18.08%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 16.55%\n",
      "Sortino: 4.14\n",
      "Max Drawdown: -2.69%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4922\n",
      "Total Return: -17.26%\n",
      "Sortino: -1.46\n",
      "Max Drawdown: -27.00%\n",
      "Number of Trades: 60\n",
      "\n",
      "Analyzing MXNUSD=X_2020-21\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (522, 6)\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (522, 9)\n",
      "Enhanced features shape before cleaning: (522, 9)\n",
      "Features shape after cleaning: (521, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -3.52%\n",
      "Sortino: -1.14\n",
      "Max Drawdown: -8.89%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 1.77%\n",
      "Sortino: -0.00\n",
      "Max Drawdown: -5.29%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6692\n",
      "Total Return: 32.31%\n",
      "Sortino: 7.69\n",
      "Max Drawdown: -1.88%\n",
      "Number of Trades: 44\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7266\n",
      "Total Return: 46.51%\n",
      "Sortino: 13.12\n",
      "Max Drawdown: -1.49%\n",
      "Number of Trades: 41\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: 11.71%\n",
      "Sortino: 2.22\n",
      "Max Drawdown: -2.03%\n",
      "Number of Trades: 63\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4609\n",
      "Total Return: -1.27%\n",
      "Sortino: -0.61\n",
      "Max Drawdown: -6.76%\n",
      "Number of Trades: 59\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6231\n",
      "Total Return: 18.84%\n",
      "Sortino: 3.75\n",
      "Max Drawdown: -2.59%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: 14.80%\n",
      "Sortino: 2.88\n",
      "Max Drawdown: -3.64%\n",
      "Number of Trades: 63\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6077\n",
      "Total Return: 19.44%\n",
      "Sortino: 4.03\n",
      "Max Drawdown: -2.92%\n",
      "Number of Trades: 73\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5391\n",
      "Total Return: 11.37%\n",
      "Sortino: 2.12\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 61\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 36.01%\n",
      "Sortino: 9.00\n",
      "Max Drawdown: -1.88%\n",
      "Number of Trades: 82\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7031\n",
      "Total Return: 41.88%\n",
      "Sortino: 11.55\n",
      "Max Drawdown: -1.54%\n",
      "Number of Trades: 66\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5308\n",
      "Total Return: 5.56%\n",
      "Sortino: 0.83\n",
      "Max Drawdown: -5.06%\n",
      "Number of Trades: 47\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6172\n",
      "Total Return: 16.30%\n",
      "Sortino: 2.93\n",
      "Max Drawdown: -3.06%\n",
      "Number of Trades: 49\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: 10.41%\n",
      "Sortino: 1.87\n",
      "Max Drawdown: -4.50%\n",
      "Number of Trades: 63\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5859\n",
      "Total Return: 17.27%\n",
      "Sortino: 3.34\n",
      "Max Drawdown: -5.04%\n",
      "Number of Trades: 53\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6385\n",
      "Total Return: 23.73%\n",
      "Sortino: 4.80\n",
      "Max Drawdown: -2.05%\n",
      "Number of Trades: 50\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6328\n",
      "Total Return: 26.63%\n",
      "Sortino: 5.23\n",
      "Max Drawdown: -2.58%\n",
      "Number of Trades: 47\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5154\n",
      "Total Return: 10.01%\n",
      "Sortino: 1.95\n",
      "Max Drawdown: -3.11%\n",
      "Number of Trades: 48\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5234\n",
      "Total Return: 10.17%\n",
      "Sortino: 1.84\n",
      "Max Drawdown: -3.78%\n",
      "Number of Trades: 57\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 17.11%\n",
      "Sortino: 3.33\n",
      "Max Drawdown: -2.73%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5859\n",
      "Total Return: 15.46%\n",
      "Sortino: 2.80\n",
      "Max Drawdown: -3.03%\n",
      "Number of Trades: 45\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 14.85%\n",
      "Sortino: 2.78\n",
      "Max Drawdown: -2.92%\n",
      "Number of Trades: 71\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6250\n",
      "Total Return: 28.58%\n",
      "Sortino: 6.70\n",
      "Max Drawdown: -1.66%\n",
      "Number of Trades: 54\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 37.03%\n",
      "Sortino: 9.25\n",
      "Max Drawdown: -1.61%\n",
      "Number of Trades: 70\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6641\n",
      "Total Return: 39.28%\n",
      "Sortino: 9.47\n",
      "Max Drawdown: -2.17%\n",
      "Number of Trades: 59\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 10.69%\n",
      "Sortino: 1.92\n",
      "Max Drawdown: -4.56%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6328\n",
      "Total Return: 21.48%\n",
      "Sortino: 3.88\n",
      "Max Drawdown: -2.59%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 6.00%\n",
      "Sortino: 0.90\n",
      "Max Drawdown: -5.81%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5938\n",
      "Total Return: 18.56%\n",
      "Sortino: 3.41\n",
      "Max Drawdown: -2.59%\n",
      "Number of Trades: 41\n",
      "\n",
      "Analyzing MXNUSD=X_2022-23\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 1.14%\n",
      "Sortino: -0.09\n",
      "Max Drawdown: -8.98%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -7.31%\n",
      "Sortino: -1.24\n",
      "Max Drawdown: -13.01%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7692\n",
      "Total Return: 64.15%\n",
      "Sortino: 12.87\n",
      "Max Drawdown: -1.79%\n",
      "Number of Trades: 47\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7266\n",
      "Total Return: 56.71%\n",
      "Sortino: 15.39\n",
      "Max Drawdown: -1.41%\n",
      "Number of Trades: 38\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5923\n",
      "Total Return: 5.23%\n",
      "Sortino: 0.51\n",
      "Max Drawdown: -6.21%\n",
      "Number of Trades: 47\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: -3.23%\n",
      "Sortino: -0.69\n",
      "Max Drawdown: -11.91%\n",
      "Number of Trades: 51\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 23.72%\n",
      "Sortino: 4.18\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 46\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: 20.63%\n",
      "Sortino: 3.65\n",
      "Max Drawdown: -4.57%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 15.74%\n",
      "Sortino: 2.47\n",
      "Max Drawdown: -2.85%\n",
      "Number of Trades: 40\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5703\n",
      "Total Return: 18.86%\n",
      "Sortino: 2.98\n",
      "Max Drawdown: -4.80%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7846\n",
      "Total Return: 63.14%\n",
      "Sortino: 11.64\n",
      "Max Drawdown: -1.93%\n",
      "Number of Trades: 69\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7578\n",
      "Total Return: 50.61%\n",
      "Sortino: 8.37\n",
      "Max Drawdown: -3.82%\n",
      "Number of Trades: 72\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5308\n",
      "Total Return: 9.55%\n",
      "Sortino: 1.38\n",
      "Max Drawdown: -4.75%\n",
      "Number of Trades: 36\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 13.30%\n",
      "Sortino: 2.02\n",
      "Max Drawdown: -5.20%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 14.18%\n",
      "Sortino: 2.21\n",
      "Max Drawdown: -4.12%\n",
      "Number of Trades: 60\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: 8.58%\n",
      "Sortino: 0.99\n",
      "Max Drawdown: -4.96%\n",
      "Number of Trades: 63\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5462\n",
      "Total Return: 8.24%\n",
      "Sortino: 1.11\n",
      "Max Drawdown: -9.35%\n",
      "Number of Trades: 49\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6328\n",
      "Total Return: 22.74%\n",
      "Sortino: 3.75\n",
      "Max Drawdown: -3.88%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4846\n",
      "Total Return: -5.68%\n",
      "Sortino: -1.21\n",
      "Max Drawdown: -9.76%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5625\n",
      "Total Return: 22.97%\n",
      "Sortino: 4.36\n",
      "Max Drawdown: -2.73%\n",
      "Number of Trades: 51\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5462\n",
      "Total Return: 8.32%\n",
      "Sortino: 1.15\n",
      "Max Drawdown: -3.90%\n",
      "Number of Trades: 41\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6250\n",
      "Total Return: 32.42%\n",
      "Sortino: 6.32\n",
      "Max Drawdown: -1.76%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 13.92%\n",
      "Sortino: 2.16\n",
      "Max Drawdown: -4.28%\n",
      "Number of Trades: 56\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5391\n",
      "Total Return: 17.32%\n",
      "Sortino: 3.00\n",
      "Max Drawdown: -4.09%\n",
      "Number of Trades: 55\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7615\n",
      "Total Return: 57.56%\n",
      "Sortino: 9.85\n",
      "Max Drawdown: -1.93%\n",
      "Number of Trades: 71\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7031\n",
      "Total Return: 46.55%\n",
      "Sortino: 8.22\n",
      "Max Drawdown: -3.82%\n",
      "Number of Trades: 73\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5154\n",
      "Total Return: 5.04%\n",
      "Sortino: 0.59\n",
      "Max Drawdown: -6.24%\n",
      "Number of Trades: 44\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5625\n",
      "Total Return: 22.94%\n",
      "Sortino: 4.20\n",
      "Max Drawdown: -4.57%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6077\n",
      "Total Return: 11.44%\n",
      "Sortino: 1.51\n",
      "Max Drawdown: -6.12%\n",
      "Number of Trades: 51\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6172\n",
      "Total Return: 23.25%\n",
      "Sortino: 3.98\n",
      "Max Drawdown: -2.17%\n",
      "Number of Trades: 30\n",
      "\n",
      "Analyzing MXNUSD=X_2018-23\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (1564, 6)\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (270, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (1564, 9)\n",
      "Enhanced features shape before cleaning: (1564, 9)\n",
      "Features shape after cleaning: (1563, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for MXNUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 19.02%\n",
      "Sortino: 0.65\n",
      "Max Drawdown: -8.98%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -17.28%\n",
      "Sortino: -1.44\n",
      "Max Drawdown: -21.68%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7980\n",
      "Total Return: 333.68%\n",
      "Sortino: 17.14\n",
      "Max Drawdown: -1.19%\n",
      "Number of Trades: 198\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8022\n",
      "Total Return: 217.97%\n",
      "Sortino: 13.72\n",
      "Max Drawdown: -1.82%\n",
      "Number of Trades: 139\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5422\n",
      "Total Return: 22.73%\n",
      "Sortino: 0.97\n",
      "Max Drawdown: -7.06%\n",
      "Number of Trades: 180\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5373\n",
      "Total Return: 27.90%\n",
      "Sortino: 1.78\n",
      "Max Drawdown: -9.26%\n",
      "Number of Trades: 110\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5575\n",
      "Total Return: 33.85%\n",
      "Sortino: 1.57\n",
      "Max Drawdown: -9.87%\n",
      "Number of Trades: 144\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5149\n",
      "Total Return: 25.04%\n",
      "Sortino: 1.58\n",
      "Max Drawdown: -8.82%\n",
      "Number of Trades: 87\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5499\n",
      "Total Return: 31.45%\n",
      "Sortino: 1.47\n",
      "Max Drawdown: -8.31%\n",
      "Number of Trades: 100\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5410\n",
      "Total Return: 43.76%\n",
      "Sortino: 2.91\n",
      "Max Drawdown: -5.81%\n",
      "Number of Trades: 66\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7647\n",
      "Total Return: 318.84%\n",
      "Sortino: 16.97\n",
      "Max Drawdown: -1.19%\n",
      "Number of Trades: 188\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7201\n",
      "Total Return: 192.44%\n",
      "Sortino: 14.70\n",
      "Max Drawdown: -2.17%\n",
      "Number of Trades: 131\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5448\n",
      "Total Return: 19.51%\n",
      "Sortino: 0.78\n",
      "Max Drawdown: -9.02%\n",
      "Number of Trades: 177\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 13.38%\n",
      "Sortino: 0.74\n",
      "Max Drawdown: -9.31%\n",
      "Number of Trades: 112\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5448\n",
      "Total Return: 29.06%\n",
      "Sortino: 1.38\n",
      "Max Drawdown: -10.12%\n",
      "Number of Trades: 182\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5746\n",
      "Total Return: 42.11%\n",
      "Sortino: 2.39\n",
      "Max Drawdown: -10.01%\n",
      "Number of Trades: 104\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5422\n",
      "Total Return: 34.69%\n",
      "Sortino: 1.67\n",
      "Max Drawdown: -12.40%\n",
      "Number of Trades: 54\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5709\n",
      "Total Return: 60.12%\n",
      "Sortino: 4.00\n",
      "Max Drawdown: -6.00%\n",
      "Number of Trades: 46\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5959\n",
      "Total Return: 54.73%\n",
      "Sortino: 2.43\n",
      "Max Drawdown: -4.38%\n",
      "Number of Trades: 75\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5746\n",
      "Total Return: 15.99%\n",
      "Sortino: 0.81\n",
      "Max Drawdown: -13.91%\n",
      "Number of Trades: 67\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5575\n",
      "Total Return: 25.82%\n",
      "Sortino: 1.15\n",
      "Max Drawdown: -7.69%\n",
      "Number of Trades: 170\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5410\n",
      "Total Return: 22.04%\n",
      "Sortino: 1.34\n",
      "Max Drawdown: -5.77%\n",
      "Number of Trades: 98\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5524\n",
      "Total Return: 35.91%\n",
      "Sortino: 1.70\n",
      "Max Drawdown: -10.15%\n",
      "Number of Trades: 154\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5821\n",
      "Total Return: 85.22%\n",
      "Sortino: 6.20\n",
      "Max Drawdown: -6.43%\n",
      "Number of Trades: 74\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7008\n",
      "Total Return: 227.09%\n",
      "Sortino: 11.27\n",
      "Max Drawdown: -1.60%\n",
      "Number of Trades: 155\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6604\n",
      "Total Return: 152.32%\n",
      "Sortino: 11.67\n",
      "Max Drawdown: -3.57%\n",
      "Number of Trades: 103\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5064\n",
      "Total Return: 11.15%\n",
      "Sortino: 0.34\n",
      "Max Drawdown: -10.37%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4776\n",
      "Total Return: 6.55%\n",
      "Sortino: 0.25\n",
      "Max Drawdown: -9.83%\n",
      "Number of Trades: 34\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5192\n",
      "Total Return: 4.24%\n",
      "Sortino: -0.07\n",
      "Max Drawdown: -14.86%\n",
      "Number of Trades: 201\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5485\n",
      "Total Return: 43.26%\n",
      "Sortino: 3.00\n",
      "Max Drawdown: -9.27%\n",
      "Number of Trades: 120\n",
      "\n",
      "Analyzing JPYUSD=X_2018\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 1.93%\n",
      "Sortino: 0.64\n",
      "Max Drawdown: -1.85%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -0.88%\n",
      "Sortino: -1.52\n",
      "Max Drawdown: -4.02%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 11.76%\n",
      "Sortino: 10.88\n",
      "Max Drawdown: -0.52%\n",
      "Number of Trades: 44\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 10.19%\n",
      "Sortino: 9.22\n",
      "Max Drawdown: -0.81%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 5.70%\n",
      "Sortino: 3.64\n",
      "Max Drawdown: -1.64%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 3.95%\n",
      "Sortino: 2.45\n",
      "Max Drawdown: -1.20%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 7.57%\n",
      "Sortino: 4.99\n",
      "Max Drawdown: -0.79%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 7.25%\n",
      "Sortino: 5.79\n",
      "Max Drawdown: -0.96%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 8.26%\n",
      "Sortino: 5.24\n",
      "Max Drawdown: -1.15%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 4.22%\n",
      "Sortino: 2.57\n",
      "Max Drawdown: -1.35%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8000\n",
      "Total Return: 14.51%\n",
      "Sortino: 14.90\n",
      "Max Drawdown: -0.70%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7937\n",
      "Total Return: 10.67%\n",
      "Sortino: 9.39\n",
      "Max Drawdown: -0.74%\n",
      "Number of Trades: 37\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 10.81%\n",
      "Sortino: 7.44\n",
      "Max Drawdown: -1.08%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 6.60%\n",
      "Sortino: 4.96\n",
      "Max Drawdown: -1.35%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 8.76%\n",
      "Sortino: 5.86\n",
      "Max Drawdown: -0.79%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7143\n",
      "Total Return: 9.47%\n",
      "Sortino: 8.43\n",
      "Max Drawdown: -0.65%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 5.96%\n",
      "Sortino: 4.09\n",
      "Max Drawdown: -1.03%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 4.75%\n",
      "Sortino: 3.29\n",
      "Max Drawdown: -0.96%\n",
      "Number of Trades: 37\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: -0.60%\n",
      "Sortino: -0.95\n",
      "Max Drawdown: -2.34%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 6.40%\n",
      "Sortino: 5.01\n",
      "Max Drawdown: -1.54%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 2.14%\n",
      "Sortino: 0.88\n",
      "Max Drawdown: -2.37%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 9.47%\n",
      "Sortino: 8.14\n",
      "Max Drawdown: -1.08%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 9.26%\n",
      "Sortino: 6.20\n",
      "Max Drawdown: -1.01%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 9.51%\n",
      "Sortino: 7.95\n",
      "Max Drawdown: -0.54%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 9.04%\n",
      "Sortino: 5.39\n",
      "Max Drawdown: -1.34%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 9.22%\n",
      "Sortino: 7.69\n",
      "Max Drawdown: -0.81%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 4.12%\n",
      "Sortino: 2.14\n",
      "Max Drawdown: -1.49%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: -1.41%\n",
      "Sortino: -1.17\n",
      "Max Drawdown: -2.17%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -2.20%\n",
      "Sortino: -1.97\n",
      "Max Drawdown: -4.27%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: -1.04%\n",
      "Sortino: -1.09\n",
      "Max Drawdown: -1.95%\n",
      "Number of Trades: 34\n",
      "\n",
      "Analyzing JPYUSD=X_2019\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (259, 6)\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (259, 9)\n",
      "Enhanced features shape before cleaning: (259, 9)\n",
      "Features shape after cleaning: (258, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -1.46%\n",
      "Sortino: -2.05\n",
      "Max Drawdown: -2.57%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 1.21%\n",
      "Sortino: 0.14\n",
      "Max Drawdown: -7.35%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 7.96%\n",
      "Sortino: 12.26\n",
      "Max Drawdown: -0.36%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8254\n",
      "Total Return: 32.15%\n",
      "Sortino: 18.59\n",
      "Max Drawdown: -0.96%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 2.39%\n",
      "Sortino: 1.51\n",
      "Max Drawdown: -1.55%\n",
      "Number of Trades: 36\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -2.33%\n",
      "Sortino: -0.96\n",
      "Max Drawdown: -7.82%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 1.58%\n",
      "Sortino: 0.63\n",
      "Max Drawdown: -1.57%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 2.84%\n",
      "Sortino: 0.61\n",
      "Max Drawdown: -5.75%\n",
      "Number of Trades: 40\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: -0.98%\n",
      "Sortino: -1.69\n",
      "Max Drawdown: -2.59%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 21.41%\n",
      "Sortino: 6.93\n",
      "Max Drawdown: -1.82%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 8.97%\n",
      "Sortino: 13.93\n",
      "Max Drawdown: -0.36%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8571\n",
      "Total Return: 36.54%\n",
      "Sortino: 41.47\n",
      "Max Drawdown: -0.36%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -1.68%\n",
      "Sortino: -2.41\n",
      "Max Drawdown: -2.61%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 8.97%\n",
      "Sortino: 2.42\n",
      "Max Drawdown: -3.78%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -2.39%\n",
      "Sortino: -2.93\n",
      "Max Drawdown: -3.77%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 22.23%\n",
      "Sortino: 10.81\n",
      "Max Drawdown: -2.94%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 1.26%\n",
      "Sortino: 0.31\n",
      "Max Drawdown: -1.31%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 1.65%\n",
      "Sortino: 0.24\n",
      "Max Drawdown: -4.83%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 1.68%\n",
      "Sortino: 0.81\n",
      "Max Drawdown: -1.87%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 1.11%\n",
      "Sortino: 0.12\n",
      "Max Drawdown: -6.26%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 0.93%\n",
      "Sortino: -0.02\n",
      "Max Drawdown: -1.56%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -5.57%\n",
      "Sortino: -1.83\n",
      "Max Drawdown: -7.49%\n",
      "Number of Trades: 34\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: -0.39%\n",
      "Sortino: -1.17\n",
      "Max Drawdown: -2.33%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7937\n",
      "Total Return: 27.42%\n",
      "Sortino: 14.92\n",
      "Max Drawdown: -1.77%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 8.22%\n",
      "Sortino: 12.12\n",
      "Max Drawdown: -0.36%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8571\n",
      "Total Return: 36.04%\n",
      "Sortino: 35.82\n",
      "Max Drawdown: -0.91%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -3.50%\n",
      "Sortino: -4.08\n",
      "Max Drawdown: -3.65%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: -8.95%\n",
      "Sortino: -2.69\n",
      "Max Drawdown: -9.68%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: -3.08%\n",
      "Sortino: -3.62\n",
      "Max Drawdown: -4.55%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: -9.94%\n",
      "Sortino: -3.17\n",
      "Max Drawdown: -10.89%\n",
      "Number of Trades: 30\n",
      "\n",
      "Analyzing JPYUSD=X_2020\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (261, 6)\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (261, 9)\n",
      "Enhanced features shape before cleaning: (261, 9)\n",
      "Features shape after cleaning: (260, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 1.90%\n",
      "Sortino: 0.59\n",
      "Max Drawdown: -2.05%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -6.41%\n",
      "Sortino: -5.06\n",
      "Max Drawdown: -6.91%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8308\n",
      "Total Return: 11.42%\n",
      "Sortino: 22.94\n",
      "Max Drawdown: -0.20%\n",
      "Number of Trades: 44\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8571\n",
      "Total Return: 13.60%\n",
      "Sortino: 19.42\n",
      "Max Drawdown: -0.42%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 5.23%\n",
      "Sortino: 4.58\n",
      "Max Drawdown: -1.54%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 1.55%\n",
      "Sortino: 0.51\n",
      "Max Drawdown: -2.16%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -0.68%\n",
      "Sortino: -1.24\n",
      "Max Drawdown: -2.63%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 5.74%\n",
      "Sortino: 4.26\n",
      "Max Drawdown: -1.05%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 1.69%\n",
      "Sortino: 0.68\n",
      "Max Drawdown: -1.82%\n",
      "Number of Trades: 17\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 6.62%\n",
      "Sortino: 5.44\n",
      "Max Drawdown: -0.94%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8308\n",
      "Total Return: 11.22%\n",
      "Sortino: 19.62\n",
      "Max Drawdown: -0.23%\n",
      "Number of Trades: 36\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 12.36%\n",
      "Sortino: 15.37\n",
      "Max Drawdown: -0.68%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 4.95%\n",
      "Sortino: 4.04\n",
      "Max Drawdown: -1.38%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 3.50%\n",
      "Sortino: 2.14\n",
      "Max Drawdown: -1.60%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 1.11%\n",
      "Sortino: 0.15\n",
      "Max Drawdown: -2.63%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 2.43%\n",
      "Sortino: 1.26\n",
      "Max Drawdown: -1.44%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7692\n",
      "Total Return: 9.22%\n",
      "Sortino: 11.69\n",
      "Max Drawdown: -0.57%\n",
      "Number of Trades: 44\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8095\n",
      "Total Return: 13.44%\n",
      "Sortino: 18.84\n",
      "Max Drawdown: -0.36%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 1.91%\n",
      "Sortino: 0.63\n",
      "Max Drawdown: -2.05%\n",
      "Number of Trades: 10\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3968\n",
      "Total Return: -3.16%\n",
      "Sortino: -3.16\n",
      "Max Drawdown: -3.64%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -0.40%\n",
      "Sortino: -1.02\n",
      "Max Drawdown: -2.54%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 1.47%\n",
      "Sortino: 0.47\n",
      "Max Drawdown: -2.14%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 1.43%\n",
      "Sortino: 0.42\n",
      "Max Drawdown: -2.15%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 4.15%\n",
      "Sortino: 2.73\n",
      "Max Drawdown: -1.08%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 8.47%\n",
      "Sortino: 9.46\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 10.29%\n",
      "Sortino: 10.41\n",
      "Max Drawdown: -0.89%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 2.25%\n",
      "Sortino: 1.24\n",
      "Max Drawdown: -1.49%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 1.81%\n",
      "Sortino: 0.77\n",
      "Max Drawdown: -1.61%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -1.17%\n",
      "Sortino: -1.33\n",
      "Max Drawdown: -3.01%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -5.90%\n",
      "Sortino: -4.42\n",
      "Max Drawdown: -5.91%\n",
      "Number of Trades: 37\n",
      "\n",
      "Analyzing JPYUSD=X_2021\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -2.47%\n",
      "Sortino: -2.27\n",
      "Max Drawdown: -3.87%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -5.49%\n",
      "Sortino: -2.93\n",
      "Max Drawdown: -7.99%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8308\n",
      "Total Return: 15.42%\n",
      "Sortino: 21.45\n",
      "Max Drawdown: -0.29%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8095\n",
      "Total Return: 19.04%\n",
      "Sortino: 24.42\n",
      "Max Drawdown: -0.33%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.3846\n",
      "Total Return: -4.22%\n",
      "Sortino: -3.54\n",
      "Max Drawdown: -5.24%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 4.23%\n",
      "Sortino: 2.40\n",
      "Max Drawdown: -2.03%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -1.18%\n",
      "Sortino: -1.45\n",
      "Max Drawdown: -3.22%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -5.80%\n",
      "Sortino: -3.09\n",
      "Max Drawdown: -7.97%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -0.54%\n",
      "Sortino: -1.00\n",
      "Max Drawdown: -3.00%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -4.25%\n",
      "Sortino: -2.38\n",
      "Max Drawdown: -8.03%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8308\n",
      "Total Return: 14.71%\n",
      "Sortino: 17.27\n",
      "Max Drawdown: -0.54%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7937\n",
      "Total Return: 17.23%\n",
      "Sortino: 14.72\n",
      "Max Drawdown: -0.59%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -2.36%\n",
      "Sortino: -2.16\n",
      "Max Drawdown: -3.78%\n",
      "Number of Trades: 5\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: -5.82%\n",
      "Sortino: -3.09\n",
      "Max Drawdown: -8.27%\n",
      "Number of Trades: 7\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4000\n",
      "Total Return: -2.54%\n",
      "Sortino: -2.45\n",
      "Max Drawdown: -3.59%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 5.31%\n",
      "Sortino: 3.43\n",
      "Max Drawdown: -2.03%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -0.68%\n",
      "Sortino: -1.14\n",
      "Max Drawdown: -2.16%\n",
      "Number of Trades: 3\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -5.53%\n",
      "Sortino: -2.93\n",
      "Max Drawdown: -7.99%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 2.40%\n",
      "Sortino: 0.96\n",
      "Max Drawdown: -2.21%\n",
      "Number of Trades: 1\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 5.61%\n",
      "Sortino: 3.51\n",
      "Max Drawdown: -2.07%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -2.52%\n",
      "Sortino: -2.27\n",
      "Max Drawdown: -3.87%\n",
      "Number of Trades: 1\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -5.53%\n",
      "Sortino: -2.93\n",
      "Max Drawdown: -7.99%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: 1.17%\n",
      "Sortino: 0.18\n",
      "Max Drawdown: -1.73%\n",
      "Number of Trades: 11\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -5.93%\n",
      "Sortino: -3.13\n",
      "Max Drawdown: -7.98%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 2.71%\n",
      "Sortino: 1.24\n",
      "Max Drawdown: -1.86%\n",
      "Number of Trades: 11\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: -2.30%\n",
      "Sortino: -1.40\n",
      "Max Drawdown: -6.70%\n",
      "Number of Trades: 9\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -2.52%\n",
      "Sortino: -2.27\n",
      "Max Drawdown: -3.87%\n",
      "Number of Trades: 1\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -5.53%\n",
      "Sortino: -2.93\n",
      "Max Drawdown: -7.99%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -0.76%\n",
      "Sortino: -1.17\n",
      "Max Drawdown: -2.16%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 0.36%\n",
      "Sortino: -0.27\n",
      "Max Drawdown: -3.01%\n",
      "Number of Trades: 32\n",
      "\n",
      "Analyzing JPYUSD=X_2022\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 7.76%\n",
      "Sortino: 2.70\n",
      "Max Drawdown: -4.25%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -1.34%\n",
      "Sortino: -0.78\n",
      "Max Drawdown: -6.82%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 17.15%\n",
      "Sortino: 4.66\n",
      "Max Drawdown: -4.36%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 14.86%\n",
      "Sortino: 6.24\n",
      "Max Drawdown: -1.54%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 10.62%\n",
      "Sortino: 3.61\n",
      "Max Drawdown: -3.38%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -6.30%\n",
      "Sortino: -2.30\n",
      "Max Drawdown: -5.93%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 10.09%\n",
      "Sortino: 3.23\n",
      "Max Drawdown: -3.42%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 7.24%\n",
      "Sortino: 2.06\n",
      "Max Drawdown: -3.22%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 15.43%\n",
      "Sortino: 5.16\n",
      "Max Drawdown: -3.42%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -5.05%\n",
      "Sortino: -1.88\n",
      "Max Drawdown: -7.18%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8308\n",
      "Total Return: 39.51%\n",
      "Sortino: 19.77\n",
      "Max Drawdown: -1.06%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 24.09%\n",
      "Sortino: 12.38\n",
      "Max Drawdown: -1.21%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 17.78%\n",
      "Sortino: 5.14\n",
      "Max Drawdown: -2.71%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: -8.75%\n",
      "Sortino: -3.09\n",
      "Max Drawdown: -10.45%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 19.22%\n",
      "Sortino: 5.69\n",
      "Max Drawdown: -2.06%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 9.22%\n",
      "Sortino: 2.62\n",
      "Max Drawdown: -3.03%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 13.29%\n",
      "Sortino: 3.37\n",
      "Max Drawdown: -4.36%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 1.74%\n",
      "Sortino: 0.32\n",
      "Max Drawdown: -5.81%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 10.52%\n",
      "Sortino: 3.61\n",
      "Max Drawdown: -2.94%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 1.54%\n",
      "Sortino: 0.26\n",
      "Max Drawdown: -4.06%\n",
      "Number of Trades: 34\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 13.18%\n",
      "Sortino: 2.85\n",
      "Max Drawdown: -6.27%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 5.45%\n",
      "Sortino: 1.36\n",
      "Max Drawdown: -6.69%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 15.43%\n",
      "Sortino: 5.70\n",
      "Max Drawdown: -2.90%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 9.00%\n",
      "Sortino: 2.70\n",
      "Max Drawdown: -4.63%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 25.88%\n",
      "Sortino: 9.59\n",
      "Max Drawdown: -1.56%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 26.28%\n",
      "Sortino: 14.07\n",
      "Max Drawdown: -1.21%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 16.53%\n",
      "Sortino: 4.63\n",
      "Max Drawdown: -3.47%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -8.33%\n",
      "Sortino: -3.00\n",
      "Max Drawdown: -10.03%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -10.43%\n",
      "Sortino: -2.46\n",
      "Max Drawdown: -12.91%\n",
      "Number of Trades: 7\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 3.16%\n",
      "Sortino: 0.75\n",
      "Max Drawdown: -4.43%\n",
      "Number of Trades: 13\n",
      "\n",
      "Analyzing JPYUSD=X_2023\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 5.61%\n",
      "Sortino: 2.49\n",
      "Max Drawdown: -2.19%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -6.72%\n",
      "Sortino: -3.71\n",
      "Max Drawdown: -6.25%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 7.22%\n",
      "Sortino: 2.58\n",
      "Max Drawdown: -2.35%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 14.74%\n",
      "Sortino: 13.03\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 11\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -7.14%\n",
      "Sortino: -2.91\n",
      "Max Drawdown: -7.40%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: -4.61%\n",
      "Sortino: -2.67\n",
      "Max Drawdown: -6.16%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 4.55%\n",
      "Sortino: 1.59\n",
      "Max Drawdown: -2.65%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 0.61%\n",
      "Sortino: -0.13\n",
      "Max Drawdown: -4.22%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 0.98%\n",
      "Sortino: 0.05\n",
      "Max Drawdown: -3.23%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 2.00%\n",
      "Sortino: 0.65\n",
      "Max Drawdown: -3.56%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7846\n",
      "Total Return: 19.05%\n",
      "Sortino: 7.13\n",
      "Max Drawdown: -1.81%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8413\n",
      "Total Return: 18.24%\n",
      "Sortino: 19.18\n",
      "Max Drawdown: -0.81%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -1.25%\n",
      "Sortino: -0.88\n",
      "Max Drawdown: -3.63%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 5.05%\n",
      "Sortino: 2.50\n",
      "Max Drawdown: -2.29%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 3.11%\n",
      "Sortino: 0.90\n",
      "Max Drawdown: -2.63%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 1.07%\n",
      "Sortino: 0.10\n",
      "Max Drawdown: -3.75%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 8.83%\n",
      "Sortino: 4.46\n",
      "Max Drawdown: -1.97%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 10.55%\n",
      "Sortino: 9.07\n",
      "Max Drawdown: -1.48%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 0.60%\n",
      "Sortino: -0.09\n",
      "Max Drawdown: -5.15%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: -5.05%\n",
      "Sortino: -2.67\n",
      "Max Drawdown: -6.97%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -3.77%\n",
      "Sortino: -1.58\n",
      "Max Drawdown: -4.53%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 7.45%\n",
      "Sortino: 4.05\n",
      "Max Drawdown: -3.13%\n",
      "Number of Trades: 5\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 8.43%\n",
      "Sortino: 3.21\n",
      "Max Drawdown: -2.88%\n",
      "Number of Trades: 36\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: -1.25%\n",
      "Sortino: -1.05\n",
      "Max Drawdown: -4.05%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8308\n",
      "Total Return: 20.93%\n",
      "Sortino: 7.44\n",
      "Max Drawdown: -1.81%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7937\n",
      "Total Return: 18.01%\n",
      "Sortino: 18.86\n",
      "Max Drawdown: -0.81%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -5.31%\n",
      "Sortino: -2.27\n",
      "Max Drawdown: -6.12%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 3.13%\n",
      "Sortino: 1.33\n",
      "Max Drawdown: -4.27%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -5.58%\n",
      "Sortino: -2.32\n",
      "Max Drawdown: -6.92%\n",
      "Number of Trades: 1\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 6.99%\n",
      "Sortino: 4.18\n",
      "Max Drawdown: -2.67%\n",
      "Number of Trades: 1\n",
      "\n",
      "Analyzing JPYUSD=X_2018-19\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -1.06%\n",
      "Sortino: -1.00\n",
      "Max Drawdown: -4.31%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 1.39%\n",
      "Sortino: -0.07\n",
      "Max Drawdown: -7.35%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 26.94%\n",
      "Sortino: 13.82\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 52\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7656\n",
      "Total Return: 52.78%\n",
      "Sortino: 21.10\n",
      "Max Drawdown: -0.79%\n",
      "Number of Trades: 62\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5462\n",
      "Total Return: 1.15%\n",
      "Sortino: -0.24\n",
      "Max Drawdown: -4.77%\n",
      "Number of Trades: 48\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6406\n",
      "Total Return: 18.22%\n",
      "Sortino: 3.55\n",
      "Max Drawdown: -3.50%\n",
      "Number of Trades: 54\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 12.53%\n",
      "Sortino: 4.29\n",
      "Max Drawdown: -2.13%\n",
      "Number of Trades: 53\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5938\n",
      "Total Return: 13.33%\n",
      "Sortino: 2.41\n",
      "Max Drawdown: -1.82%\n",
      "Number of Trades: 53\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 10.81%\n",
      "Sortino: 3.31\n",
      "Max Drawdown: -2.24%\n",
      "Number of Trades: 53\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6250\n",
      "Total Return: 18.59%\n",
      "Sortino: 3.69\n",
      "Max Drawdown: -3.33%\n",
      "Number of Trades: 49\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7692\n",
      "Total Return: 31.31%\n",
      "Sortino: 21.26\n",
      "Max Drawdown: -0.36%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8281\n",
      "Total Return: 58.58%\n",
      "Sortino: 26.97\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 63\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 2.01%\n",
      "Sortino: 0.02\n",
      "Max Drawdown: -5.42%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -8.98%\n",
      "Sortino: -1.89\n",
      "Max Drawdown: -13.94%\n",
      "Number of Trades: 41\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 8.82%\n",
      "Sortino: 2.38\n",
      "Max Drawdown: -2.51%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6406\n",
      "Total Return: 23.86%\n",
      "Sortino: 5.35\n",
      "Max Drawdown: -2.74%\n",
      "Number of Trades: 56\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5923\n",
      "Total Return: 11.71%\n",
      "Sortino: 3.45\n",
      "Max Drawdown: -2.84%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5938\n",
      "Total Return: 9.90%\n",
      "Sortino: 1.43\n",
      "Max Drawdown: -4.03%\n",
      "Number of Trades: 44\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5462\n",
      "Total Return: 2.85%\n",
      "Sortino: 0.28\n",
      "Max Drawdown: -3.13%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5234\n",
      "Total Return: 7.27%\n",
      "Sortino: 1.10\n",
      "Max Drawdown: -5.79%\n",
      "Number of Trades: 39\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 3.36%\n",
      "Sortino: 0.45\n",
      "Max Drawdown: -1.95%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: 10.19%\n",
      "Sortino: 1.79\n",
      "Max Drawdown: -5.05%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 11.63%\n",
      "Sortino: 3.24\n",
      "Max Drawdown: -1.54%\n",
      "Number of Trades: 51\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6641\n",
      "Total Return: 28.11%\n",
      "Sortino: 6.21\n",
      "Max Drawdown: -2.95%\n",
      "Number of Trades: 52\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7769\n",
      "Total Return: 29.06%\n",
      "Sortino: 15.33\n",
      "Max Drawdown: -0.89%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7656\n",
      "Total Return: 48.52%\n",
      "Sortino: 16.50\n",
      "Max Drawdown: -1.00%\n",
      "Number of Trades: 69\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 2.10%\n",
      "Sortino: 0.06\n",
      "Max Drawdown: -4.58%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -8.16%\n",
      "Sortino: -1.75\n",
      "Max Drawdown: -12.55%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5154\n",
      "Total Return: 2.89%\n",
      "Sortino: 0.31\n",
      "Max Drawdown: -3.06%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5625\n",
      "Total Return: 8.13%\n",
      "Sortino: 1.32\n",
      "Max Drawdown: -5.05%\n",
      "Number of Trades: 25\n",
      "\n",
      "Analyzing JPYUSD=X_2020-21\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (522, 6)\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (522, 9)\n",
      "Enhanced features shape before cleaning: (522, 9)\n",
      "Features shape after cleaning: (521, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -3.18%\n",
      "Sortino: -1.88\n",
      "Max Drawdown: -5.56%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -15.57%\n",
      "Sortino: -3.40\n",
      "Max Drawdown: -16.70%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8385\n",
      "Total Return: 29.71%\n",
      "Sortino: 16.95\n",
      "Max Drawdown: -0.53%\n",
      "Number of Trades: 64\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8047\n",
      "Total Return: 56.90%\n",
      "Sortino: 24.75\n",
      "Max Drawdown: -0.63%\n",
      "Number of Trades: 71\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6231\n",
      "Total Return: 5.67%\n",
      "Sortino: 1.15\n",
      "Max Drawdown: -2.21%\n",
      "Number of Trades: 36\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 17.84%\n",
      "Sortino: 3.83\n",
      "Max Drawdown: -3.15%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: 4.89%\n",
      "Sortino: 1.00\n",
      "Max Drawdown: -4.02%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4453\n",
      "Total Return: -16.42%\n",
      "Sortino: -3.58\n",
      "Max Drawdown: -17.50%\n",
      "Number of Trades: 3\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 5.89%\n",
      "Sortino: 1.29\n",
      "Max Drawdown: -2.21%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 17.84%\n",
      "Sortino: 3.83\n",
      "Max Drawdown: -3.15%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8462\n",
      "Total Return: 31.10%\n",
      "Sortino: 19.06\n",
      "Max Drawdown: -0.41%\n",
      "Number of Trades: 61\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6484\n",
      "Total Return: 29.58%\n",
      "Sortino: 7.27\n",
      "Max Drawdown: -2.25%\n",
      "Number of Trades: 46\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5769\n",
      "Total Return: 4.80%\n",
      "Sortino: 0.96\n",
      "Max Drawdown: -3.74%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4297\n",
      "Total Return: -17.80%\n",
      "Sortino: -3.83\n",
      "Max Drawdown: -18.86%\n",
      "Number of Trades: 11\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 0.98%\n",
      "Sortino: -0.32\n",
      "Max Drawdown: -4.36%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4688\n",
      "Total Return: -13.05%\n",
      "Sortino: -2.87\n",
      "Max Drawdown: -14.17%\n",
      "Number of Trades: 7\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7154\n",
      "Total Return: 23.12%\n",
      "Sortino: 10.87\n",
      "Max Drawdown: -0.99%\n",
      "Number of Trades: 58\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6250\n",
      "Total Return: 21.80%\n",
      "Sortino: 4.84\n",
      "Max Drawdown: -4.38%\n",
      "Number of Trades: 40\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 1.30%\n",
      "Sortino: -0.21\n",
      "Max Drawdown: -4.02%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4531\n",
      "Total Return: -15.61%\n",
      "Sortino: -3.40\n",
      "Max Drawdown: -16.70%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 1.93%\n",
      "Sortino: -0.00\n",
      "Max Drawdown: -4.16%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4531\n",
      "Total Return: -15.61%\n",
      "Sortino: -3.40\n",
      "Max Drawdown: -16.70%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 7.64%\n",
      "Sortino: 2.03\n",
      "Max Drawdown: -1.73%\n",
      "Number of Trades: 41\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4688\n",
      "Total Return: -11.16%\n",
      "Sortino: -2.58\n",
      "Max Drawdown: -14.31%\n",
      "Number of Trades: 40\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8154\n",
      "Total Return: 27.69%\n",
      "Sortino: 13.97\n",
      "Max Drawdown: -0.93%\n",
      "Number of Trades: 69\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7656\n",
      "Total Return: 42.42%\n",
      "Sortino: 10.04\n",
      "Max Drawdown: -2.15%\n",
      "Number of Trades: 57\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -2.91%\n",
      "Sortino: -1.60\n",
      "Max Drawdown: -4.02%\n",
      "Number of Trades: 7\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4531\n",
      "Total Return: -15.61%\n",
      "Sortino: -3.40\n",
      "Max Drawdown: -16.70%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5923\n",
      "Total Return: 5.17%\n",
      "Sortino: 1.15\n",
      "Max Drawdown: -2.82%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 2.28%\n",
      "Sortino: 0.11\n",
      "Max Drawdown: -4.80%\n",
      "Number of Trades: 63\n",
      "\n",
      "Analyzing JPYUSD=X_2022-23\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 2.39%\n",
      "Sortino: 0.13\n",
      "Max Drawdown: -8.95%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -12.14%\n",
      "Sortino: -3.73\n",
      "Max Drawdown: -11.50%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 8.32%\n",
      "Sortino: 1.41\n",
      "Max Drawdown: -3.27%\n",
      "Number of Trades: 9\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6797\n",
      "Total Return: 21.02%\n",
      "Sortino: 4.97\n",
      "Max Drawdown: -2.67%\n",
      "Number of Trades: 7\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 5.09%\n",
      "Sortino: 0.66\n",
      "Max Drawdown: -4.62%\n",
      "Number of Trades: 39\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6797\n",
      "Total Return: 18.61%\n",
      "Sortino: 3.67\n",
      "Max Drawdown: -3.01%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5769\n",
      "Total Return: 5.78%\n",
      "Sortino: 0.80\n",
      "Max Drawdown: -4.72%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6719\n",
      "Total Return: 12.87%\n",
      "Sortino: 2.41\n",
      "Max Drawdown: -2.72%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5769\n",
      "Total Return: 3.73%\n",
      "Sortino: 0.37\n",
      "Max Drawdown: -5.83%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7031\n",
      "Total Return: 22.84%\n",
      "Sortino: 5.16\n",
      "Max Drawdown: -1.89%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7000\n",
      "Total Return: 35.14%\n",
      "Sortino: 8.04\n",
      "Max Drawdown: -1.81%\n",
      "Number of Trades: 45\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7344\n",
      "Total Return: 34.88%\n",
      "Sortino: 11.14\n",
      "Max Drawdown: -1.35%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 3.84%\n",
      "Sortino: 0.38\n",
      "Max Drawdown: -5.97%\n",
      "Number of Trades: 38\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6953\n",
      "Total Return: 18.57%\n",
      "Sortino: 3.67\n",
      "Max Drawdown: -3.34%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 7.30%\n",
      "Sortino: 1.07\n",
      "Max Drawdown: -4.51%\n",
      "Number of Trades: 38\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6328\n",
      "Total Return: 7.68%\n",
      "Sortino: 1.30\n",
      "Max Drawdown: -3.58%\n",
      "Number of Trades: 48\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5308\n",
      "Total Return: -2.32%\n",
      "Sortino: -0.84\n",
      "Max Drawdown: -5.76%\n",
      "Number of Trades: 17\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6562\n",
      "Total Return: 11.91%\n",
      "Sortino: 2.26\n",
      "Max Drawdown: -3.01%\n",
      "Number of Trades: 5\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -7.23%\n",
      "Sortino: -1.78\n",
      "Max Drawdown: -7.77%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6328\n",
      "Total Return: 7.74%\n",
      "Sortino: 1.36\n",
      "Max Drawdown: -5.70%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5154\n",
      "Total Return: -5.87%\n",
      "Sortino: -1.54\n",
      "Max Drawdown: -7.79%\n",
      "Number of Trades: 17\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6641\n",
      "Total Return: 15.15%\n",
      "Sortino: 3.19\n",
      "Max Drawdown: -3.88%\n",
      "Number of Trades: 11\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 11.03%\n",
      "Sortino: 1.76\n",
      "Max Drawdown: -3.01%\n",
      "Number of Trades: 40\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6094\n",
      "Total Return: 6.78%\n",
      "Sortino: 1.14\n",
      "Max Drawdown: -6.56%\n",
      "Number of Trades: 43\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7538\n",
      "Total Return: 40.43%\n",
      "Sortino: 7.98\n",
      "Max Drawdown: -1.81%\n",
      "Number of Trades: 45\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7500\n",
      "Total Return: 35.87%\n",
      "Sortino: 11.61\n",
      "Max Drawdown: -1.35%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: -0.24%\n",
      "Sortino: -0.39\n",
      "Max Drawdown: -4.54%\n",
      "Number of Trades: 11\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6797\n",
      "Total Return: 19.84%\n",
      "Sortino: 4.27\n",
      "Max Drawdown: -3.01%\n",
      "Number of Trades: 11\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4846\n",
      "Total Return: -9.22%\n",
      "Sortino: -2.19\n",
      "Max Drawdown: -11.18%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6328\n",
      "Total Return: 8.96%\n",
      "Sortino: 1.73\n",
      "Max Drawdown: -3.85%\n",
      "Number of Trades: 23\n",
      "\n",
      "Analyzing JPYUSD=X_2018-23\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (1564, 6)\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (269, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (1564, 9)\n",
      "Enhanced features shape before cleaning: (1564, 9)\n",
      "Features shape after cleaning: (1563, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for JPYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -3.40%\n",
      "Sortino: -0.57\n",
      "Max Drawdown: -15.58%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -10.79%\n",
      "Sortino: -1.58\n",
      "Max Drawdown: -12.04%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7928\n",
      "Total Return: 309.95%\n",
      "Sortino: 12.63\n",
      "Max Drawdown: -1.89%\n",
      "Number of Trades: 182\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7566\n",
      "Total Return: 124.80%\n",
      "Sortino: 12.77\n",
      "Max Drawdown: -1.35%\n",
      "Number of Trades: 105\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5652\n",
      "Total Return: 35.74%\n",
      "Sortino: 1.58\n",
      "Max Drawdown: -6.94%\n",
      "Number of Trades: 127\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6255\n",
      "Total Return: 31.23%\n",
      "Sortino: 2.28\n",
      "Max Drawdown: -4.77%\n",
      "Number of Trades: 73\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5601\n",
      "Total Return: 16.41%\n",
      "Sortino: 0.57\n",
      "Max Drawdown: -5.78%\n",
      "Number of Trades: 115\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5993\n",
      "Total Return: 13.25%\n",
      "Sortino: 0.78\n",
      "Max Drawdown: -4.93%\n",
      "Number of Trades: 71\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5754\n",
      "Total Return: 30.22%\n",
      "Sortino: 1.25\n",
      "Max Drawdown: -6.90%\n",
      "Number of Trades: 99\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6030\n",
      "Total Return: 9.51%\n",
      "Sortino: 0.44\n",
      "Max Drawdown: -6.14%\n",
      "Number of Trades: 53\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7187\n",
      "Total Return: 241.74%\n",
      "Sortino: 10.30\n",
      "Max Drawdown: -1.81%\n",
      "Number of Trades: 118\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6891\n",
      "Total Return: 91.54%\n",
      "Sortino: 8.71\n",
      "Max Drawdown: -1.66%\n",
      "Number of Trades: 65\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5269\n",
      "Total Return: -12.31%\n",
      "Sortino: -0.92\n",
      "Max Drawdown: -19.16%\n",
      "Number of Trades: 111\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6105\n",
      "Total Return: 11.56%\n",
      "Sortino: 0.61\n",
      "Max Drawdown: -10.31%\n",
      "Number of Trades: 71\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5524\n",
      "Total Return: 11.94%\n",
      "Sortino: 0.35\n",
      "Max Drawdown: -14.64%\n",
      "Number of Trades: 147\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5843\n",
      "Total Return: 8.45%\n",
      "Sortino: 0.40\n",
      "Max Drawdown: -6.70%\n",
      "Number of Trades: 83\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6215\n",
      "Total Return: 83.52%\n",
      "Sortino: 3.64\n",
      "Max Drawdown: -4.99%\n",
      "Number of Trades: 108\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6442\n",
      "Total Return: 55.10%\n",
      "Sortino: 4.62\n",
      "Max Drawdown: -3.76%\n",
      "Number of Trades: 47\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5754\n",
      "Total Return: 18.98%\n",
      "Sortino: 0.68\n",
      "Max Drawdown: -11.99%\n",
      "Number of Trades: 99\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5993\n",
      "Total Return: 19.46%\n",
      "Sortino: 1.31\n",
      "Max Drawdown: -5.63%\n",
      "Number of Trades: 57\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5345\n",
      "Total Return: -5.71%\n",
      "Sortino: -0.57\n",
      "Max Drawdown: -14.04%\n",
      "Number of Trades: 77\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5918\n",
      "Total Return: 7.78%\n",
      "Sortino: 0.32\n",
      "Max Drawdown: -16.96%\n",
      "Number of Trades: 39\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5985\n",
      "Total Return: 53.34%\n",
      "Sortino: 2.33\n",
      "Max Drawdown: -7.42%\n",
      "Number of Trades: 113\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6067\n",
      "Total Return: 29.53%\n",
      "Sortino: 2.20\n",
      "Max Drawdown: -4.37%\n",
      "Number of Trades: 77\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7289\n",
      "Total Return: 249.81%\n",
      "Sortino: 10.22\n",
      "Max Drawdown: -1.81%\n",
      "Number of Trades: 146\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7004\n",
      "Total Return: 111.59%\n",
      "Sortino: 11.91\n",
      "Max Drawdown: -1.66%\n",
      "Number of Trades: 73\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5601\n",
      "Total Return: -0.52%\n",
      "Sortino: -0.28\n",
      "Max Drawdown: -13.40%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6142\n",
      "Total Return: 18.09%\n",
      "Sortino: 1.12\n",
      "Max Drawdown: -11.81%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5217\n",
      "Total Return: -15.03%\n",
      "Sortino: -1.10\n",
      "Max Drawdown: -17.55%\n",
      "Number of Trades: 89\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5581\n",
      "Total Return: 1.67%\n",
      "Sortino: -0.17\n",
      "Max Drawdown: -13.00%\n",
      "Number of Trades: 59\n",
      "\n",
      "Analyzing CNYUSD=X_2018\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -0.26%\n",
      "Sortino: -1.01\n",
      "Max Drawdown: -1.66%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 2.07%\n",
      "Sortino: 1.22\n",
      "Max Drawdown: -1.35%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 4.25%\n",
      "Sortino: 3.41\n",
      "Max Drawdown: -0.78%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 0.71%\n",
      "Sortino: -0.23\n",
      "Max Drawdown: -1.42%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -4.10%\n",
      "Sortino: -3.70\n",
      "Max Drawdown: -5.50%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -4.21%\n",
      "Sortino: -4.78\n",
      "Max Drawdown: -4.78%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 1.02%\n",
      "Sortino: 0.06\n",
      "Max Drawdown: -1.84%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 0.16%\n",
      "Sortino: -0.79\n",
      "Max Drawdown: -1.71%\n",
      "Number of Trades: 8\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: -1.71%\n",
      "Sortino: -1.75\n",
      "Max Drawdown: -4.22%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: -0.46%\n",
      "Sortino: -1.43\n",
      "Max Drawdown: -2.22%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 7.41%\n",
      "Sortino: 9.60\n",
      "Max Drawdown: -0.47%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 4.71%\n",
      "Sortino: 5.29\n",
      "Max Drawdown: -0.64%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -0.62%\n",
      "Sortino: -1.12\n",
      "Max Drawdown: -1.84%\n",
      "Number of Trades: 7\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 0.18%\n",
      "Sortino: -0.77\n",
      "Max Drawdown: -1.97%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: -0.85%\n",
      "Sortino: -1.38\n",
      "Max Drawdown: -3.36%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 0.49%\n",
      "Sortino: -0.44\n",
      "Max Drawdown: -2.08%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: -0.97%\n",
      "Sortino: -1.44\n",
      "Max Drawdown: -3.13%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: -2.07%\n",
      "Sortino: -3.10\n",
      "Max Drawdown: -2.19%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: -0.77%\n",
      "Sortino: -1.28\n",
      "Max Drawdown: -3.15%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -1.99%\n",
      "Sortino: -2.89\n",
      "Max Drawdown: -3.05%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -4.07%\n",
      "Sortino: -3.51\n",
      "Max Drawdown: -5.97%\n",
      "Number of Trades: 11\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -0.46%\n",
      "Sortino: -1.47\n",
      "Max Drawdown: -2.26%\n",
      "Number of Trades: 14\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -1.91%\n",
      "Sortino: -2.02\n",
      "Max Drawdown: -3.13%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 1.65%\n",
      "Sortino: 0.82\n",
      "Max Drawdown: -1.60%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 9.90%\n",
      "Sortino: 18.28\n",
      "Max Drawdown: -0.27%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7937\n",
      "Total Return: 6.56%\n",
      "Sortino: 7.19\n",
      "Max Drawdown: -1.21%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -1.66%\n",
      "Sortino: -1.78\n",
      "Max Drawdown: -2.78%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: -1.06%\n",
      "Sortino: -1.93\n",
      "Max Drawdown: -2.78%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: -0.88%\n",
      "Sortino: -1.38\n",
      "Max Drawdown: -2.77%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -3.01%\n",
      "Sortino: -3.78\n",
      "Max Drawdown: -3.12%\n",
      "Number of Trades: 30\n",
      "\n",
      "Analyzing CNYUSD=X_2019\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (258, 6)\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (258, 9)\n",
      "Enhanced features shape before cleaning: (258, 9)\n",
      "Features shape after cleaning: (257, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 1.79%\n",
      "Sortino: 1.23\n",
      "Max Drawdown: -0.92%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -1.88%\n",
      "Sortino: -1.93\n",
      "Max Drawdown: -3.58%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: 1.29%\n",
      "Sortino: 0.53\n",
      "Max Drawdown: -0.91%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7143\n",
      "Total Return: 9.13%\n",
      "Sortino: 12.85\n",
      "Max Drawdown: -0.73%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4531\n",
      "Total Return: -1.87%\n",
      "Sortino: -3.35\n",
      "Max Drawdown: -2.61%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: -2.08%\n",
      "Sortino: -2.28\n",
      "Max Drawdown: -3.32%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4844\n",
      "Total Return: -0.69%\n",
      "Sortino: -2.04\n",
      "Max Drawdown: -1.46%\n",
      "Number of Trades: 9\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 3.65%\n",
      "Sortino: 2.14\n",
      "Max Drawdown: -1.55%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -0.43%\n",
      "Sortino: -1.71\n",
      "Max Drawdown: -1.34%\n",
      "Number of Trades: 11\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 3.11%\n",
      "Sortino: 2.09\n",
      "Max Drawdown: -2.20%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7812\n",
      "Total Return: 6.16%\n",
      "Sortino: 18.22\n",
      "Max Drawdown: -0.13%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 10.38%\n",
      "Sortino: 17.20\n",
      "Max Drawdown: -0.40%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: 1.06%\n",
      "Sortino: 0.18\n",
      "Max Drawdown: -1.37%\n",
      "Number of Trades: 17\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 1.16%\n",
      "Sortino: 0.22\n",
      "Max Drawdown: -3.54%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5781\n",
      "Total Return: 0.18%\n",
      "Sortino: -0.98\n",
      "Max Drawdown: -1.10%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 2.18%\n",
      "Sortino: 1.11\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: 0.53%\n",
      "Sortino: -0.64\n",
      "Max Drawdown: -1.45%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 0.14%\n",
      "Sortino: -0.53\n",
      "Max Drawdown: -2.80%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5156\n",
      "Total Return: -0.14%\n",
      "Sortino: -1.35\n",
      "Max Drawdown: -1.35%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 0.11%\n",
      "Sortino: -0.61\n",
      "Max Drawdown: -3.05%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4531\n",
      "Total Return: -1.36%\n",
      "Sortino: -2.93\n",
      "Max Drawdown: -2.00%\n",
      "Number of Trades: 9\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 2.67%\n",
      "Sortino: 1.43\n",
      "Max Drawdown: -1.72%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 0.46%\n",
      "Sortino: -0.64\n",
      "Max Drawdown: -1.10%\n",
      "Number of Trades: 17\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 4.86%\n",
      "Sortino: 3.83\n",
      "Max Drawdown: -2.12%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7500\n",
      "Total Return: 5.48%\n",
      "Sortino: 12.72\n",
      "Max Drawdown: -0.28%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 11.80%\n",
      "Sortino: 22.33\n",
      "Max Drawdown: -0.40%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5156\n",
      "Total Return: -0.21%\n",
      "Sortino: -1.45\n",
      "Max Drawdown: -1.13%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 4.86%\n",
      "Sortino: 4.09\n",
      "Max Drawdown: -1.76%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4844\n",
      "Total Return: -1.29%\n",
      "Sortino: -2.70\n",
      "Max Drawdown: -2.05%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: -1.94%\n",
      "Sortino: -1.95\n",
      "Max Drawdown: -4.64%\n",
      "Number of Trades: 33\n",
      "\n",
      "Analyzing CNYUSD=X_2020\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (261, 6)\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (261, 9)\n",
      "Enhanced features shape before cleaning: (261, 9)\n",
      "Features shape after cleaning: (260, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 4.28%\n",
      "Sortino: 3.40\n",
      "Max Drawdown: -1.16%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -0.59%\n",
      "Sortino: -1.60\n",
      "Max Drawdown: -2.19%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 5.08%\n",
      "Sortino: 5.56\n",
      "Max Drawdown: -0.94%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 2.64%\n",
      "Sortino: 2.29\n",
      "Max Drawdown: -1.00%\n",
      "Number of Trades: 4\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -1.04%\n",
      "Sortino: -1.93\n",
      "Max Drawdown: -3.54%\n",
      "Number of Trades: 9\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -0.68%\n",
      "Sortino: -1.58\n",
      "Max Drawdown: -1.31%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -3.53%\n",
      "Sortino: -3.27\n",
      "Max Drawdown: -3.90%\n",
      "Number of Trades: 2\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 0.50%\n",
      "Sortino: -0.39\n",
      "Max Drawdown: -1.00%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -0.84%\n",
      "Sortino: -1.71\n",
      "Max Drawdown: -3.24%\n",
      "Number of Trades: 4\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 0.50%\n",
      "Sortino: -0.39\n",
      "Max Drawdown: -1.00%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 1.59%\n",
      "Sortino: 0.48\n",
      "Max Drawdown: -1.44%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 2.89%\n",
      "Sortino: 2.62\n",
      "Max Drawdown: -1.00%\n",
      "Number of Trades: 6\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -2.60%\n",
      "Sortino: -3.16\n",
      "Max Drawdown: -4.28%\n",
      "Number of Trades: 6\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 0.81%\n",
      "Sortino: -0.09\n",
      "Max Drawdown: -1.00%\n",
      "Number of Trades: 5\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -3.60%\n",
      "Sortino: -3.36\n",
      "Max Drawdown: -4.04%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -1.47%\n",
      "Sortino: -2.00\n",
      "Max Drawdown: -2.14%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 1.34%\n",
      "Sortino: 0.45\n",
      "Max Drawdown: -1.82%\n",
      "Number of Trades: 11\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 2.70%\n",
      "Sortino: 2.37\n",
      "Max Drawdown: -1.00%\n",
      "Number of Trades: 2\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -4.21%\n",
      "Sortino: -3.77\n",
      "Max Drawdown: -3.90%\n",
      "Number of Trades: 1\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 0.50%\n",
      "Sortino: -0.39\n",
      "Max Drawdown: -1.00%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: -1.34%\n",
      "Sortino: -1.54\n",
      "Max Drawdown: -1.91%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: -4.07%\n",
      "Sortino: -4.17\n",
      "Max Drawdown: -3.14%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -0.44%\n",
      "Sortino: -1.34\n",
      "Max Drawdown: -3.23%\n",
      "Number of Trades: 8\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 0.57%\n",
      "Sortino: -0.32\n",
      "Max Drawdown: -1.00%\n",
      "Number of Trades: 3\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 7.16%\n",
      "Sortino: 7.33\n",
      "Max Drawdown: -1.08%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 5.79%\n",
      "Sortino: 7.37\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -3.66%\n",
      "Sortino: -3.41\n",
      "Max Drawdown: -4.03%\n",
      "Number of Trades: 4\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 0.75%\n",
      "Sortino: -0.16\n",
      "Max Drawdown: -1.00%\n",
      "Number of Trades: 3\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -2.95%\n",
      "Sortino: -2.82\n",
      "Max Drawdown: -3.32%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 0.74%\n",
      "Sortino: -0.17\n",
      "Max Drawdown: -0.57%\n",
      "Number of Trades: 15\n",
      "\n",
      "Analyzing CNYUSD=X_2021\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 1.61%\n",
      "Sortino: 1.11\n",
      "Max Drawdown: -0.51%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 0.14%\n",
      "Sortino: -1.01\n",
      "Max Drawdown: -0.99%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8154\n",
      "Total Return: 4.40%\n",
      "Sortino: 15.23\n",
      "Max Drawdown: -0.25%\n",
      "Number of Trades: 38\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 4.81%\n",
      "Sortino: 13.05\n",
      "Max Drawdown: -0.19%\n",
      "Number of Trades: 34\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 2.39%\n",
      "Sortino: 3.45\n",
      "Max Drawdown: -0.50%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3968\n",
      "Total Return: -0.45%\n",
      "Sortino: -2.62\n",
      "Max Drawdown: -1.16%\n",
      "Number of Trades: 3\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 2.17%\n",
      "Sortino: 2.95\n",
      "Max Drawdown: -0.54%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4127\n",
      "Total Return: -0.81%\n",
      "Sortino: -3.24\n",
      "Max Drawdown: -1.17%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 2.28%\n",
      "Sortino: 3.16\n",
      "Max Drawdown: -0.50%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -0.13%\n",
      "Sortino: -2.00\n",
      "Max Drawdown: -1.07%\n",
      "Number of Trades: 3\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7538\n",
      "Total Return: 3.75%\n",
      "Sortino: 9.81\n",
      "Max Drawdown: -0.24%\n",
      "Number of Trades: 41\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 3.71%\n",
      "Sortino: 8.37\n",
      "Max Drawdown: -0.35%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 2.39%\n",
      "Sortino: 3.45\n",
      "Max Drawdown: -0.50%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -0.13%\n",
      "Sortino: -2.00\n",
      "Max Drawdown: -1.07%\n",
      "Number of Trades: 3\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 1.17%\n",
      "Sortino: 0.38\n",
      "Max Drawdown: -1.10%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -1.28%\n",
      "Sortino: -3.65\n",
      "Max Drawdown: -1.63%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 2.91%\n",
      "Sortino: 5.41\n",
      "Max Drawdown: -0.43%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: 1.21%\n",
      "Sortino: 0.61\n",
      "Max Drawdown: -0.89%\n",
      "Number of Trades: 9\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 1.17%\n",
      "Sortino: 0.39\n",
      "Max Drawdown: -0.76%\n",
      "Number of Trades: 10\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4127\n",
      "Total Return: -0.21%\n",
      "Sortino: -2.16\n",
      "Max Drawdown: -1.16%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 1.75%\n",
      "Sortino: 1.62\n",
      "Max Drawdown: -0.96%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -0.72%\n",
      "Sortino: -2.90\n",
      "Max Drawdown: -1.17%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 1.76%\n",
      "Sortino: 1.48\n",
      "Max Drawdown: -1.10%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: -1.21%\n",
      "Sortino: -3.55\n",
      "Max Drawdown: -1.56%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 2.92%\n",
      "Sortino: 5.67\n",
      "Max Drawdown: -0.38%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 1.64%\n",
      "Sortino: 1.71\n",
      "Max Drawdown: -0.89%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 1.69%\n",
      "Sortino: 1.52\n",
      "Max Drawdown: -0.50%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -0.13%\n",
      "Sortino: -2.00\n",
      "Max Drawdown: -1.07%\n",
      "Number of Trades: 3\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 0.63%\n",
      "Sortino: -0.63\n",
      "Max Drawdown: -1.05%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -1.37%\n",
      "Sortino: -2.94\n",
      "Max Drawdown: -1.75%\n",
      "Number of Trades: 30\n",
      "\n",
      "Analyzing CNYUSD=X_2022\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 2.10%\n",
      "Sortino: 0.71\n",
      "Max Drawdown: -2.59%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 0.17%\n",
      "Sortino: -0.53\n",
      "Max Drawdown: -3.76%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 3.02%\n",
      "Sortino: 0.95\n",
      "Max Drawdown: -3.96%\n",
      "Number of Trades: 5\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 8.14%\n",
      "Sortino: 5.65\n",
      "Max Drawdown: -1.72%\n",
      "Number of Trades: 11\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: -2.93%\n",
      "Sortino: -1.69\n",
      "Max Drawdown: -4.28%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 2.94%\n",
      "Sortino: 1.28\n",
      "Max Drawdown: -2.34%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 0.84%\n",
      "Sortino: -0.02\n",
      "Max Drawdown: -4.05%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: -0.11%\n",
      "Sortino: -0.55\n",
      "Max Drawdown: -2.10%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 7.49%\n",
      "Sortino: 4.40\n",
      "Max Drawdown: -2.08%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 4.36%\n",
      "Sortino: 2.14\n",
      "Max Drawdown: -2.10%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 21.28%\n",
      "Sortino: 24.05\n",
      "Max Drawdown: -0.52%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7937\n",
      "Total Return: 15.54%\n",
      "Sortino: 21.02\n",
      "Max Drawdown: -0.37%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 7.74%\n",
      "Sortino: 4.44\n",
      "Max Drawdown: -3.22%\n",
      "Number of Trades: 17\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 4.29%\n",
      "Sortino: 2.19\n",
      "Max Drawdown: -2.19%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: 1.12%\n",
      "Sortino: 0.12\n",
      "Max Drawdown: -2.71%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 3.84%\n",
      "Sortino: 1.86\n",
      "Max Drawdown: -1.71%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: -2.28%\n",
      "Sortino: -1.23\n",
      "Max Drawdown: -5.10%\n",
      "Number of Trades: 1\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -0.32%\n",
      "Sortino: -0.72\n",
      "Max Drawdown: -3.09%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: -2.28%\n",
      "Sortino: -1.23\n",
      "Max Drawdown: -5.10%\n",
      "Number of Trades: 1\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -0.32%\n",
      "Sortino: -0.72\n",
      "Max Drawdown: -3.09%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -2.89%\n",
      "Sortino: -1.53\n",
      "Max Drawdown: -5.10%\n",
      "Number of Trades: 7\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -2.61%\n",
      "Sortino: -2.08\n",
      "Max Drawdown: -3.95%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: 2.55%\n",
      "Sortino: 1.03\n",
      "Max Drawdown: -2.90%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 5.78%\n",
      "Sortino: 2.75\n",
      "Max Drawdown: -2.10%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 17.95%\n",
      "Sortino: 11.33\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7143\n",
      "Total Return: 9.74%\n",
      "Sortino: 6.80\n",
      "Max Drawdown: -0.81%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: -2.60%\n",
      "Sortino: -1.37\n",
      "Max Drawdown: -5.28%\n",
      "Number of Trades: 5\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -3.21%\n",
      "Sortino: -2.54\n",
      "Max Drawdown: -4.07%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -3.73%\n",
      "Sortino: -1.90\n",
      "Max Drawdown: -4.98%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -2.27%\n",
      "Sortino: -1.92\n",
      "Max Drawdown: -3.13%\n",
      "Number of Trades: 21\n",
      "\n",
      "Analyzing CNYUSD=X_2023\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 2.24%\n",
      "Sortino: 0.48\n",
      "Max Drawdown: -2.15%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -2.18%\n",
      "Sortino: -2.07\n",
      "Max Drawdown: -2.08%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 10.98%\n",
      "Sortino: 3.21\n",
      "Max Drawdown: -2.28%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 8.67%\n",
      "Sortino: 16.90\n",
      "Max Drawdown: -0.44%\n",
      "Number of Trades: 9\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 12.13%\n",
      "Sortino: 5.35\n",
      "Max Drawdown: -2.00%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 5.44%\n",
      "Sortino: 5.35\n",
      "Max Drawdown: -1.03%\n",
      "Number of Trades: 10\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 17.88%\n",
      "Sortino: 7.27\n",
      "Max Drawdown: -1.62%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 1.73%\n",
      "Sortino: 0.56\n",
      "Max Drawdown: -2.58%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 10.40%\n",
      "Sortino: 3.41\n",
      "Max Drawdown: -1.93%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4286\n",
      "Total Return: -0.52%\n",
      "Sortino: -1.15\n",
      "Max Drawdown: -2.36%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 30.17%\n",
      "Sortino: 43.71\n",
      "Max Drawdown: -0.64%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 9.33%\n",
      "Sortino: 20.20\n",
      "Max Drawdown: -0.44%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 11.47%\n",
      "Sortino: 4.12\n",
      "Max Drawdown: -2.07%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 2.62%\n",
      "Sortino: 1.69\n",
      "Max Drawdown: -2.03%\n",
      "Number of Trades: 12\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 18.30%\n",
      "Sortino: 8.02\n",
      "Max Drawdown: -0.99%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 1.55%\n",
      "Sortino: 0.51\n",
      "Max Drawdown: -2.41%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 10.48%\n",
      "Sortino: 3.11\n",
      "Max Drawdown: -2.26%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 1.85%\n",
      "Sortino: 0.86\n",
      "Max Drawdown: -2.55%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -4.99%\n",
      "Sortino: -1.70\n",
      "Max Drawdown: -4.46%\n",
      "Number of Trades: 7\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 5.02%\n",
      "Sortino: 4.30\n",
      "Max Drawdown: -1.03%\n",
      "Number of Trades: 7\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 10.05%\n",
      "Sortino: 3.52\n",
      "Max Drawdown: -2.42%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 5.53%\n",
      "Sortino: 5.20\n",
      "Max Drawdown: -1.03%\n",
      "Number of Trades: 6\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 21.07%\n",
      "Sortino: 10.08\n",
      "Max Drawdown: -0.94%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 2.88%\n",
      "Sortino: 1.86\n",
      "Max Drawdown: -1.86%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 15.00%\n",
      "Sortino: 5.17\n",
      "Max Drawdown: -2.56%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 9.95%\n",
      "Sortino: 23.61\n",
      "Max Drawdown: -0.30%\n",
      "Number of Trades: 25\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 1.53%\n",
      "Sortino: 0.24\n",
      "Max Drawdown: -4.16%\n",
      "Number of Trades: 11\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -5.57%\n",
      "Sortino: -3.80\n",
      "Max Drawdown: -7.19%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -2.62%\n",
      "Sortino: -1.02\n",
      "Max Drawdown: -3.65%\n",
      "Number of Trades: 1\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 2.08%\n",
      "Sortino: 0.84\n",
      "Max Drawdown: -1.63%\n",
      "Number of Trades: 1\n",
      "\n",
      "Analyzing CNYUSD=X_2018-19\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (519, 6)\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (519, 9)\n",
      "Enhanced features shape before cleaning: (519, 9)\n",
      "Features shape after cleaning: (518, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -1.85%\n",
      "Sortino: -1.63\n",
      "Max Drawdown: -4.56%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -1.62%\n",
      "Sortino: -1.43\n",
      "Max Drawdown: -4.32%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 6.16%\n",
      "Sortino: 1.95\n",
      "Max Drawdown: -1.57%\n",
      "Number of Trades: 43\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7188\n",
      "Total Return: 13.78%\n",
      "Sortino: 7.44\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 61\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4846\n",
      "Total Return: 0.25%\n",
      "Sortino: -0.95\n",
      "Max Drawdown: -2.40%\n",
      "Number of Trades: 57\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4766\n",
      "Total Return: -1.68%\n",
      "Sortino: -1.55\n",
      "Max Drawdown: -5.06%\n",
      "Number of Trades: 47\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -2.05%\n",
      "Sortino: -1.77\n",
      "Max Drawdown: -4.04%\n",
      "Number of Trades: 69\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5938\n",
      "Total Return: -1.02%\n",
      "Sortino: -1.12\n",
      "Max Drawdown: -3.62%\n",
      "Number of Trades: 49\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -2.71%\n",
      "Sortino: -2.14\n",
      "Max Drawdown: -4.15%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 3.95%\n",
      "Sortino: 1.01\n",
      "Max Drawdown: -1.82%\n",
      "Number of Trades: 34\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7308\n",
      "Total Return: 12.45%\n",
      "Sortino: 9.31\n",
      "Max Drawdown: -0.88%\n",
      "Number of Trades: 61\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7031\n",
      "Total Return: 16.47%\n",
      "Sortino: 10.89\n",
      "Max Drawdown: -0.98%\n",
      "Number of Trades: 66\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4846\n",
      "Total Return: 1.69%\n",
      "Sortino: -0.17\n",
      "Max Drawdown: -3.09%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 6.41%\n",
      "Sortino: 2.43\n",
      "Max Drawdown: -1.55%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: 0.55%\n",
      "Sortino: -0.61\n",
      "Max Drawdown: -2.81%\n",
      "Number of Trades: 57\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5625\n",
      "Total Return: -1.23%\n",
      "Sortino: -1.23\n",
      "Max Drawdown: -3.93%\n",
      "Number of Trades: 41\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: -0.71%\n",
      "Sortino: -1.22\n",
      "Max Drawdown: -2.37%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6328\n",
      "Total Return: 2.90%\n",
      "Sortino: 0.40\n",
      "Max Drawdown: -2.78%\n",
      "Number of Trades: 69\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -1.77%\n",
      "Sortino: -1.62\n",
      "Max Drawdown: -3.34%\n",
      "Number of Trades: 61\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5078\n",
      "Total Return: -2.59%\n",
      "Sortino: -1.76\n",
      "Max Drawdown: -4.17%\n",
      "Number of Trades: 39\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5462\n",
      "Total Return: 0.13%\n",
      "Sortino: -0.80\n",
      "Max Drawdown: -2.46%\n",
      "Number of Trades: 49\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5781\n",
      "Total Return: -1.55%\n",
      "Sortino: -1.36\n",
      "Max Drawdown: -3.53%\n",
      "Number of Trades: 39\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5923\n",
      "Total Return: 0.68%\n",
      "Sortino: -0.54\n",
      "Max Drawdown: -2.57%\n",
      "Number of Trades: 60\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4922\n",
      "Total Return: -5.05%\n",
      "Sortino: -2.74\n",
      "Max Drawdown: -6.47%\n",
      "Number of Trades: 48\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7923\n",
      "Total Return: 14.43%\n",
      "Sortino: 12.09\n",
      "Max Drawdown: -0.97%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7734\n",
      "Total Return: 20.38%\n",
      "Sortino: 18.86\n",
      "Max Drawdown: -0.50%\n",
      "Number of Trades: 63\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4615\n",
      "Total Return: -1.51%\n",
      "Sortino: -1.57\n",
      "Max Drawdown: -3.09%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5234\n",
      "Total Return: 5.42%\n",
      "Sortino: 1.89\n",
      "Max Drawdown: -1.55%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 0.28%\n",
      "Sortino: -0.78\n",
      "Max Drawdown: -1.83%\n",
      "Number of Trades: 51\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4688\n",
      "Total Return: -3.09%\n",
      "Sortino: -2.09\n",
      "Max Drawdown: -5.47%\n",
      "Number of Trades: 63\n",
      "\n",
      "Analyzing CNYUSD=X_2020-21\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (522, 6)\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (522, 9)\n",
      "Enhanced features shape before cleaning: (522, 9)\n",
      "Features shape after cleaning: (521, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 1.40%\n",
      "Sortino: -0.50\n",
      "Max Drawdown: -0.76%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -5.14%\n",
      "Sortino: -2.55\n",
      "Max Drawdown: -7.07%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7308\n",
      "Total Return: 5.42%\n",
      "Sortino: 3.22\n",
      "Max Drawdown: -0.62%\n",
      "Number of Trades: 68\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6797\n",
      "Total Return: -0.11%\n",
      "Sortino: -0.71\n",
      "Max Drawdown: -6.06%\n",
      "Number of Trades: 56\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 0.61%\n",
      "Sortino: -1.09\n",
      "Max Drawdown: -1.34%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4844\n",
      "Total Return: -4.60%\n",
      "Sortino: -2.54\n",
      "Max Drawdown: -6.59%\n",
      "Number of Trades: 51\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6692\n",
      "Total Return: 3.81%\n",
      "Sortino: 1.63\n",
      "Max Drawdown: -0.99%\n",
      "Number of Trades: 53\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5078\n",
      "Total Return: 1.12%\n",
      "Sortino: -0.36\n",
      "Max Drawdown: -2.75%\n",
      "Number of Trades: 40\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6846\n",
      "Total Return: 4.77%\n",
      "Sortino: 2.80\n",
      "Max Drawdown: -1.08%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4844\n",
      "Total Return: -0.07%\n",
      "Sortino: -0.93\n",
      "Max Drawdown: -3.72%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7538\n",
      "Total Return: 7.76%\n",
      "Sortino: 8.24\n",
      "Max Drawdown: -0.37%\n",
      "Number of Trades: 72\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7656\n",
      "Total Return: 17.03%\n",
      "Sortino: 10.80\n",
      "Max Drawdown: -0.66%\n",
      "Number of Trades: 70\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6846\n",
      "Total Return: 5.40%\n",
      "Sortino: 3.35\n",
      "Max Drawdown: -0.50%\n",
      "Number of Trades: 55\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4844\n",
      "Total Return: 1.61%\n",
      "Sortino: -0.17\n",
      "Max Drawdown: -3.76%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 5.15%\n",
      "Sortino: 2.96\n",
      "Max Drawdown: -1.02%\n",
      "Number of Trades: 57\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4688\n",
      "Total Return: -0.92%\n",
      "Sortino: -1.39\n",
      "Max Drawdown: -2.84%\n",
      "Number of Trades: 54\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 0.65%\n",
      "Sortino: -1.09\n",
      "Max Drawdown: -1.46%\n",
      "Number of Trades: 63\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: -6.27%\n",
      "Sortino: -3.08\n",
      "Max Drawdown: -7.95%\n",
      "Number of Trades: 52\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5462\n",
      "Total Return: -0.16%\n",
      "Sortino: -1.76\n",
      "Max Drawdown: -1.73%\n",
      "Number of Trades: 61\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: -6.25%\n",
      "Sortino: -3.11\n",
      "Max Drawdown: -8.11%\n",
      "Number of Trades: 54\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6538\n",
      "Total Return: 4.15%\n",
      "Sortino: 2.15\n",
      "Max Drawdown: -0.85%\n",
      "Number of Trades: 63\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4844\n",
      "Total Return: -5.00%\n",
      "Sortino: -2.80\n",
      "Max Drawdown: -6.28%\n",
      "Number of Trades: 34\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 4.21%\n",
      "Sortino: 2.08\n",
      "Max Drawdown: -1.03%\n",
      "Number of Trades: 63\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5156\n",
      "Total Return: -2.07%\n",
      "Sortino: -1.77\n",
      "Max Drawdown: -4.12%\n",
      "Number of Trades: 50\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 6.74%\n",
      "Sortino: 6.21\n",
      "Max Drawdown: -0.36%\n",
      "Number of Trades: 82\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6875\n",
      "Total Return: 17.20%\n",
      "Sortino: 10.42\n",
      "Max Drawdown: -0.95%\n",
      "Number of Trades: 61\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5308\n",
      "Total Return: -1.11%\n",
      "Sortino: -2.49\n",
      "Max Drawdown: -2.22%\n",
      "Number of Trades: 55\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4297\n",
      "Total Return: -6.61%\n",
      "Sortino: -3.46\n",
      "Max Drawdown: -7.50%\n",
      "Number of Trades: 14\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 1.56%\n",
      "Sortino: -0.36\n",
      "Max Drawdown: -1.28%\n",
      "Number of Trades: 67\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: -1.11%\n",
      "Sortino: -1.30\n",
      "Max Drawdown: -5.13%\n",
      "Number of Trades: 48\n",
      "\n",
      "Analyzing CNYUSD=X_2022-23\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 1.56%\n",
      "Sortino: -0.03\n",
      "Max Drawdown: -3.17%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -2.68%\n",
      "Sortino: -2.28\n",
      "Max Drawdown: -2.56%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 24.40%\n",
      "Sortino: 3.86\n",
      "Max Drawdown: -2.77%\n",
      "Number of Trades: 38\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6172\n",
      "Total Return: 9.29%\n",
      "Sortino: 8.56\n",
      "Max Drawdown: -0.51%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -14.70%\n",
      "Sortino: -2.60\n",
      "Max Drawdown: -20.26%\n",
      "Number of Trades: 46\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5781\n",
      "Total Return: 0.91%\n",
      "Sortino: -0.52\n",
      "Max Drawdown: -2.29%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -12.56%\n",
      "Sortino: -2.24\n",
      "Max Drawdown: -18.93%\n",
      "Number of Trades: 42\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5625\n",
      "Total Return: 0.34%\n",
      "Sortino: -0.81\n",
      "Max Drawdown: -2.62%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -6.60%\n",
      "Sortino: -1.32\n",
      "Max Drawdown: -16.91%\n",
      "Number of Trades: 46\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: -0.04%\n",
      "Sortino: -1.00\n",
      "Max Drawdown: -2.62%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: -1.56%\n",
      "Sortino: -0.49\n",
      "Max Drawdown: -17.56%\n",
      "Number of Trades: 46\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6094\n",
      "Total Return: 8.42%\n",
      "Sortino: 7.12\n",
      "Max Drawdown: -0.51%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -9.58%\n",
      "Sortino: -1.81\n",
      "Max Drawdown: -18.93%\n",
      "Number of Trades: 44\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: -0.79%\n",
      "Sortino: -1.35\n",
      "Max Drawdown: -2.91%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: -7.72%\n",
      "Sortino: -1.48\n",
      "Max Drawdown: -15.72%\n",
      "Number of Trades: 42\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5703\n",
      "Total Return: 1.05%\n",
      "Sortino: -0.45\n",
      "Max Drawdown: -2.45%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -13.34%\n",
      "Sortino: -2.38\n",
      "Max Drawdown: -19.43%\n",
      "Number of Trades: 38\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5625\n",
      "Total Return: -0.81%\n",
      "Sortino: -1.34\n",
      "Max Drawdown: -2.55%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4769\n",
      "Total Return: -13.44%\n",
      "Sortino: -2.50\n",
      "Max Drawdown: -19.37%\n",
      "Number of Trades: 47\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5625\n",
      "Total Return: -0.66%\n",
      "Sortino: -1.28\n",
      "Max Drawdown: -2.91%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5154\n",
      "Total Return: -14.26%\n",
      "Sortino: -2.51\n",
      "Max Drawdown: -19.25%\n",
      "Number of Trades: 40\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5625\n",
      "Total Return: -0.40%\n",
      "Sortino: -1.15\n",
      "Max Drawdown: -2.55%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 12.05%\n",
      "Sortino: 1.86\n",
      "Max Drawdown: -2.90%\n",
      "Number of Trades: 48\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6250\n",
      "Total Return: 9.44%\n",
      "Sortino: 9.50\n",
      "Max Drawdown: -0.68%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5615\n",
      "Total Return: -1.87%\n",
      "Sortino: -0.54\n",
      "Max Drawdown: -17.49%\n",
      "Number of Trades: 50\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6484\n",
      "Total Return: 9.47%\n",
      "Sortino: 8.17\n",
      "Max Drawdown: -0.72%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5308\n",
      "Total Return: -0.04%\n",
      "Sortino: -0.31\n",
      "Max Drawdown: -6.93%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: -0.94%\n",
      "Sortino: -1.41\n",
      "Max Drawdown: -3.19%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: -1.65%\n",
      "Sortino: -0.54\n",
      "Max Drawdown: -4.11%\n",
      "Number of Trades: 20\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: -2.50%\n",
      "Sortino: -1.81\n",
      "Max Drawdown: -3.93%\n",
      "Number of Trades: 25\n",
      "\n",
      "Analyzing CNYUSD=X_2018-23\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (1563, 6)\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (270, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (1563, 9)\n",
      "Enhanced features shape before cleaning: (1563, 9)\n",
      "Features shape after cleaning: (1562, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CNYUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -6.17%\n",
      "Sortino: -1.05\n",
      "Max Drawdown: -8.65%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -3.59%\n",
      "Sortino: -1.70\n",
      "Max Drawdown: -4.41%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7698\n",
      "Total Return: 125.79%\n",
      "Sortino: 7.31\n",
      "Max Drawdown: -2.57%\n",
      "Number of Trades: 223\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7201\n",
      "Total Return: 32.21%\n",
      "Sortino: 15.35\n",
      "Max Drawdown: -0.44%\n",
      "Number of Trades: 89\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5371\n",
      "Total Return: 10.68%\n",
      "Sortino: 0.39\n",
      "Max Drawdown: -5.44%\n",
      "Number of Trades: 184\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5410\n",
      "Total Return: -10.11%\n",
      "Sortino: -2.71\n",
      "Max Drawdown: -10.42%\n",
      "Number of Trades: 138\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5780\n",
      "Total Return: 51.18%\n",
      "Sortino: 3.78\n",
      "Max Drawdown: -5.92%\n",
      "Number of Trades: 198\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4440\n",
      "Total Return: 8.74%\n",
      "Sortino: 1.42\n",
      "Max Drawdown: -3.26%\n",
      "Number of Trades: 78\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6138\n",
      "Total Return: 37.02%\n",
      "Sortino: 2.13\n",
      "Max Drawdown: -4.12%\n",
      "Number of Trades: 158\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6455\n",
      "Total Return: 13.08%\n",
      "Sortino: 1.93\n",
      "Max Drawdown: -2.03%\n",
      "Number of Trades: 75\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7749\n",
      "Total Return: 135.74%\n",
      "Sortino: 8.00\n",
      "Max Drawdown: -3.82%\n",
      "Number of Trades: 215\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7985\n",
      "Total Return: 36.53%\n",
      "Sortino: 20.94\n",
      "Max Drawdown: -0.30%\n",
      "Number of Trades: 105\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5575\n",
      "Total Return: 46.28%\n",
      "Sortino: 3.82\n",
      "Max Drawdown: -5.48%\n",
      "Number of Trades: 160\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5149\n",
      "Total Return: 16.22%\n",
      "Sortino: 4.71\n",
      "Max Drawdown: -2.14%\n",
      "Number of Trades: 90\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6777\n",
      "Total Return: 72.00%\n",
      "Sortino: 4.59\n",
      "Max Drawdown: -3.85%\n",
      "Number of Trades: 204\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5149\n",
      "Total Return: 8.89%\n",
      "Sortino: 1.52\n",
      "Max Drawdown: -3.14%\n",
      "Number of Trades: 128\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5959\n",
      "Total Return: 29.77%\n",
      "Sortino: 1.82\n",
      "Max Drawdown: -3.07%\n",
      "Number of Trades: 206\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6157\n",
      "Total Return: 7.61%\n",
      "Sortino: 0.82\n",
      "Max Drawdown: -2.10%\n",
      "Number of Trades: 72\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4987\n",
      "Total Return: -13.83%\n",
      "Sortino: -1.56\n",
      "Max Drawdown: -17.52%\n",
      "Number of Trades: 162\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4963\n",
      "Total Return: -3.53%\n",
      "Sortino: -1.80\n",
      "Max Drawdown: -3.38%\n",
      "Number of Trades: 129\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5780\n",
      "Total Return: 35.23%\n",
      "Sortino: 2.44\n",
      "Max Drawdown: -5.34%\n",
      "Number of Trades: 196\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5037\n",
      "Total Return: 4.53%\n",
      "Sortino: 0.10\n",
      "Max Drawdown: -2.36%\n",
      "Number of Trades: 122\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6445\n",
      "Total Return: 51.62%\n",
      "Sortino: 2.95\n",
      "Max Drawdown: -11.45%\n",
      "Number of Trades: 202\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5560\n",
      "Total Return: 16.16%\n",
      "Sortino: 4.47\n",
      "Max Drawdown: -3.23%\n",
      "Number of Trades: 134\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7826\n",
      "Total Return: 167.53%\n",
      "Sortino: 15.62\n",
      "Max Drawdown: -2.72%\n",
      "Number of Trades: 205\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6679\n",
      "Total Return: 30.05%\n",
      "Sortino: 15.75\n",
      "Max Drawdown: -1.00%\n",
      "Number of Trades: 118\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5064\n",
      "Total Return: 11.44%\n",
      "Sortino: 0.49\n",
      "Max Drawdown: -8.42%\n",
      "Number of Trades: 122\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4440\n",
      "Total Return: 1.99%\n",
      "Sortino: -0.57\n",
      "Max Drawdown: -2.74%\n",
      "Number of Trades: 59\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5294\n",
      "Total Return: -6.13%\n",
      "Sortino: -0.90\n",
      "Max Drawdown: -17.43%\n",
      "Number of Trades: 144\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6007\n",
      "Total Return: 0.20%\n",
      "Sortino: -0.81\n",
      "Max Drawdown: -3.54%\n",
      "Number of Trades: 70\n",
      "\n",
      "Analyzing CHFUSD=X_2018\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2018-12-31\n",
      "Future Testing Period: 2019-01-01 to 2019-04-02\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -1.74%\n",
      "Sortino: -2.11\n",
      "Max Drawdown: -2.87%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -1.29%\n",
      "Sortino: -1.67\n",
      "Max Drawdown: -3.64%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7846\n",
      "Total Return: 11.87%\n",
      "Sortino: 13.79\n",
      "Max Drawdown: -0.67%\n",
      "Number of Trades: 36\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8254\n",
      "Total Return: 10.34%\n",
      "Sortino: 9.65\n",
      "Max Drawdown: -0.77%\n",
      "Number of Trades: 37\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 1.87%\n",
      "Sortino: 0.79\n",
      "Max Drawdown: -1.41%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -1.65%\n",
      "Sortino: -1.73\n",
      "Max Drawdown: -2.96%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 6.07%\n",
      "Sortino: 4.94\n",
      "Max Drawdown: -0.87%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -0.05%\n",
      "Sortino: -0.71\n",
      "Max Drawdown: -2.58%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 4.85%\n",
      "Sortino: 3.71\n",
      "Max Drawdown: -1.50%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 0.75%\n",
      "Sortino: -0.11\n",
      "Max Drawdown: -2.05%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7538\n",
      "Total Return: 9.57%\n",
      "Sortino: 9.57\n",
      "Max Drawdown: -0.67%\n",
      "Number of Trades: 36\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 8.56%\n",
      "Sortino: 7.15\n",
      "Max Drawdown: -0.81%\n",
      "Number of Trades: 37\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 5.99%\n",
      "Sortino: 4.90\n",
      "Max Drawdown: -0.85%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 0.72%\n",
      "Sortino: -0.14\n",
      "Max Drawdown: -3.18%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 7.95%\n",
      "Sortino: 7.41\n",
      "Max Drawdown: -0.85%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 0.78%\n",
      "Sortino: -0.10\n",
      "Max Drawdown: -1.37%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -1.24%\n",
      "Sortino: -1.67\n",
      "Max Drawdown: -2.33%\n",
      "Number of Trades: 5\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -1.02%\n",
      "Sortino: -1.40\n",
      "Max Drawdown: -3.64%\n",
      "Number of Trades: 5\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 5.99%\n",
      "Sortino: 4.79\n",
      "Max Drawdown: -1.25%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -0.42%\n",
      "Sortino: -0.95\n",
      "Max Drawdown: -3.30%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 4.68%\n",
      "Sortino: 3.67\n",
      "Max Drawdown: -1.54%\n",
      "Number of Trades: 14\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -1.04%\n",
      "Sortino: -1.42\n",
      "Max Drawdown: -3.90%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 6.09%\n",
      "Sortino: 5.44\n",
      "Max Drawdown: -0.92%\n",
      "Number of Trades: 28\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 4.06%\n",
      "Sortino: 2.77\n",
      "Max Drawdown: -1.31%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 5.83%\n",
      "Sortino: 4.30\n",
      "Max Drawdown: -0.92%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 4.22%\n",
      "Sortino: 2.97\n",
      "Max Drawdown: -1.23%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 7.03%\n",
      "Sortino: 6.64\n",
      "Max Drawdown: -0.85%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 2.68%\n",
      "Sortino: 1.41\n",
      "Max Drawdown: -1.50%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: -0.16%\n",
      "Sortino: -0.88\n",
      "Max Drawdown: -3.45%\n",
      "Number of Trades: 34\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 1.05%\n",
      "Sortino: 0.13\n",
      "Max Drawdown: -1.64%\n",
      "Number of Trades: 28\n",
      "\n",
      "Analyzing CHFUSD=X_2019\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (259, 6)\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (259, 9)\n",
      "Enhanced features shape before cleaning: (259, 9)\n",
      "Features shape after cleaning: (258, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2019-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-04-01\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 0.96%\n",
      "Sortino: -0.03\n",
      "Max Drawdown: -1.52%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 1.79%\n",
      "Sortino: 0.45\n",
      "Max Drawdown: -6.20%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8615\n",
      "Total Return: 11.59%\n",
      "Sortino: 20.53\n",
      "Max Drawdown: -0.24%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8254\n",
      "Total Return: 18.99%\n",
      "Sortino: 13.12\n",
      "Max Drawdown: -1.18%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 3.14%\n",
      "Sortino: 1.99\n",
      "Max Drawdown: -1.28%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: 0.35%\n",
      "Sortino: -0.23\n",
      "Max Drawdown: -6.27%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 5.65%\n",
      "Sortino: 5.11\n",
      "Max Drawdown: -1.01%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: -8.59%\n",
      "Sortino: -3.66\n",
      "Max Drawdown: -10.73%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 5.00%\n",
      "Sortino: 4.53\n",
      "Max Drawdown: -0.91%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 1.25%\n",
      "Sortino: 0.20\n",
      "Max Drawdown: -6.55%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8308\n",
      "Total Return: 10.12%\n",
      "Sortino: 14.81\n",
      "Max Drawdown: -0.33%\n",
      "Number of Trades: 43\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7937\n",
      "Total Return: 17.71%\n",
      "Sortino: 14.77\n",
      "Max Drawdown: -1.27%\n",
      "Number of Trades: 30\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 5.38%\n",
      "Sortino: 4.64\n",
      "Max Drawdown: -1.23%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: 1.25%\n",
      "Sortino: 0.20\n",
      "Max Drawdown: -6.55%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 7.52%\n",
      "Sortino: 7.62\n",
      "Max Drawdown: -0.65%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: -2.54%\n",
      "Sortino: -1.37\n",
      "Max Drawdown: -6.20%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 6.94%\n",
      "Sortino: 7.20\n",
      "Max Drawdown: -0.86%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7143\n",
      "Total Return: 3.09%\n",
      "Sortino: 0.73\n",
      "Max Drawdown: -2.94%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 4.77%\n",
      "Sortino: 4.15\n",
      "Max Drawdown: -1.03%\n",
      "Number of Trades: 43\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.3492\n",
      "Total Return: -12.88%\n",
      "Sortino: -5.70\n",
      "Max Drawdown: -12.71%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 5.21%\n",
      "Sortino: 4.84\n",
      "Max Drawdown: -0.94%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: -1.80%\n",
      "Sortino: -1.00\n",
      "Max Drawdown: -5.87%\n",
      "Number of Trades: 7\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7538\n",
      "Total Return: 9.42%\n",
      "Sortino: 13.45\n",
      "Max Drawdown: -0.35%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 2.78%\n",
      "Sortino: 0.91\n",
      "Max Drawdown: -6.20%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 7.89%\n",
      "Sortino: 9.09\n",
      "Max Drawdown: -0.52%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7143\n",
      "Total Return: 14.70%\n",
      "Sortino: 10.96\n",
      "Max Drawdown: -1.27%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 2.81%\n",
      "Sortino: 1.72\n",
      "Max Drawdown: -0.97%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -1.61%\n",
      "Sortino: -1.13\n",
      "Max Drawdown: -6.55%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 0.54%\n",
      "Sortino: -0.37\n",
      "Max Drawdown: -2.30%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4127\n",
      "Total Return: -11.89%\n",
      "Sortino: -4.96\n",
      "Max Drawdown: -12.18%\n",
      "Number of Trades: 27\n",
      "\n",
      "Analyzing CHFUSD=X_2020\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (261, 6)\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (261, 9)\n",
      "Enhanced features shape before cleaning: (261, 9)\n",
      "Features shape after cleaning: (260, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 365\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2020-12-31\n",
      "Future Testing Period: 2021-01-01 to 2021-04-02\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 3.51%\n",
      "Sortino: 1.65\n",
      "Max Drawdown: -1.88%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -6.22%\n",
      "Sortino: -3.95\n",
      "Max Drawdown: -6.76%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7846\n",
      "Total Return: 12.13%\n",
      "Sortino: 13.83\n",
      "Max Drawdown: -0.67%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 17.03%\n",
      "Sortino: 19.30\n",
      "Max Drawdown: -0.60%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 2.90%\n",
      "Sortino: 1.65\n",
      "Max Drawdown: -1.65%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 4.45%\n",
      "Sortino: 2.29\n",
      "Max Drawdown: -1.58%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 0.88%\n",
      "Sortino: -0.03\n",
      "Max Drawdown: -2.61%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 0.09%\n",
      "Sortino: -0.44\n",
      "Max Drawdown: -4.02%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 1.08%\n",
      "Sortino: 0.11\n",
      "Max Drawdown: -2.61%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 3.84%\n",
      "Sortino: 1.81\n",
      "Max Drawdown: -2.65%\n",
      "Number of Trades: 13\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7846\n",
      "Total Return: 12.62%\n",
      "Sortino: 13.82\n",
      "Max Drawdown: -0.44%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7937\n",
      "Total Return: 15.47%\n",
      "Sortino: 13.50\n",
      "Max Drawdown: -0.61%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 2.61%\n",
      "Sortino: 1.33\n",
      "Max Drawdown: -2.61%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 2.98%\n",
      "Sortino: 1.36\n",
      "Max Drawdown: -2.74%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: 1.79%\n",
      "Sortino: 0.64\n",
      "Max Drawdown: -3.01%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 3.07%\n",
      "Sortino: 1.38\n",
      "Max Drawdown: -1.91%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 3.37%\n",
      "Sortino: 1.41\n",
      "Max Drawdown: -2.44%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 5.89%\n",
      "Sortino: 2.95\n",
      "Max Drawdown: -1.58%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: 1.32%\n",
      "Sortino: 0.29\n",
      "Max Drawdown: -2.61%\n",
      "Number of Trades: 10\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5397\n",
      "Total Return: 0.13%\n",
      "Sortino: -0.41\n",
      "Max Drawdown: -4.32%\n",
      "Number of Trades: 12\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 1.03%\n",
      "Sortino: 0.08\n",
      "Max Drawdown: -2.61%\n",
      "Number of Trades: 12\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -1.11%\n",
      "Sortino: -1.11\n",
      "Max Drawdown: -4.22%\n",
      "Number of Trades: 16\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 1.27%\n",
      "Sortino: 0.24\n",
      "Max Drawdown: -3.57%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5714\n",
      "Total Return: 0.81%\n",
      "Sortino: -0.04\n",
      "Max Drawdown: -3.67%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 5.41%\n",
      "Sortino: 3.45\n",
      "Max Drawdown: -2.61%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 10.92%\n",
      "Sortino: 7.85\n",
      "Max Drawdown: -0.77%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4000\n",
      "Total Return: -1.42%\n",
      "Sortino: -1.81\n",
      "Max Drawdown: -4.44%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: 0.03%\n",
      "Sortino: -0.48\n",
      "Max Drawdown: -3.91%\n",
      "Number of Trades: 12\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: -4.51%\n",
      "Sortino: -3.13\n",
      "Max Drawdown: -4.99%\n",
      "Number of Trades: 22\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -2.41%\n",
      "Sortino: -1.75\n",
      "Max Drawdown: -4.86%\n",
      "Number of Trades: 22\n",
      "\n",
      "Analyzing CHFUSD=X_2021\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2021-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-04-02\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 1.89%\n",
      "Sortino: 0.60\n",
      "Max Drawdown: -2.77%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -0.50%\n",
      "Sortino: -0.87\n",
      "Max Drawdown: -3.24%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 12.65%\n",
      "Sortino: 14.31\n",
      "Max Drawdown: -0.57%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7778\n",
      "Total Return: 16.62%\n",
      "Sortino: 15.61\n",
      "Max Drawdown: -0.97%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 9.37%\n",
      "Sortino: 7.60\n",
      "Max Drawdown: -1.01%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 11.55%\n",
      "Sortino: 7.98\n",
      "Max Drawdown: -1.24%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 11.52%\n",
      "Sortino: 11.38\n",
      "Max Drawdown: -0.64%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 14.61%\n",
      "Sortino: 13.78\n",
      "Max Drawdown: -0.85%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 10.58%\n",
      "Sortino: 9.74\n",
      "Max Drawdown: -0.60%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 12.64%\n",
      "Sortino: 9.75\n",
      "Max Drawdown: -1.39%\n",
      "Number of Trades: 37\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 11.95%\n",
      "Sortino: 13.51\n",
      "Max Drawdown: -0.57%\n",
      "Number of Trades: 35\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8730\n",
      "Total Return: 19.91%\n",
      "Sortino: 30.58\n",
      "Max Drawdown: -0.42%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 6.30%\n",
      "Sortino: 4.07\n",
      "Max Drawdown: -1.27%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 7.29%\n",
      "Sortino: 4.43\n",
      "Max Drawdown: -2.74%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 11.46%\n",
      "Sortino: 12.31\n",
      "Max Drawdown: -0.80%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 13.60%\n",
      "Sortino: 11.18\n",
      "Max Drawdown: -1.49%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 4.42%\n",
      "Sortino: 2.77\n",
      "Max Drawdown: -1.45%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 11.03%\n",
      "Sortino: 8.49\n",
      "Max Drawdown: -0.52%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4308\n",
      "Total Return: 1.51%\n",
      "Sortino: 0.46\n",
      "Max Drawdown: -2.92%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 6.71%\n",
      "Sortino: 3.44\n",
      "Max Drawdown: -1.80%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 5.58%\n",
      "Sortino: 3.65\n",
      "Max Drawdown: -0.96%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 14.14%\n",
      "Sortino: 12.33\n",
      "Max Drawdown: -0.49%\n",
      "Number of Trades: 24\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 9.58%\n",
      "Sortino: 8.86\n",
      "Max Drawdown: -0.82%\n",
      "Number of Trades: 39\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 13.59%\n",
      "Sortino: 10.95\n",
      "Max Drawdown: -0.99%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 12.70%\n",
      "Sortino: 15.23\n",
      "Max Drawdown: -0.48%\n",
      "Number of Trades: 36\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7619\n",
      "Total Return: 15.52%\n",
      "Sortino: 13.26\n",
      "Max Drawdown: -0.65%\n",
      "Number of Trades: 35\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -0.44%\n",
      "Sortino: -0.85\n",
      "Max Drawdown: -2.30%\n",
      "Number of Trades: 18\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 1.24%\n",
      "Sortino: 0.21\n",
      "Max Drawdown: -3.37%\n",
      "Number of Trades: 29\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -5.03%\n",
      "Sortino: -3.49\n",
      "Max Drawdown: -5.55%\n",
      "Number of Trades: 32\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5079\n",
      "Total Return: -0.10%\n",
      "Sortino: -0.55\n",
      "Max Drawdown: -4.46%\n",
      "Number of Trades: 27\n",
      "\n",
      "Analyzing CHFUSD=X_2022\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2022-12-31\n",
      "Future Testing Period: 2023-01-01 to 2023-04-02\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 5.09%\n",
      "Sortino: 1.79\n",
      "Max Drawdown: -3.43%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 0.64%\n",
      "Sortino: -0.09\n",
      "Max Drawdown: -3.71%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.9077\n",
      "Total Return: 34.38%\n",
      "Sortino: 27.77\n",
      "Max Drawdown: -0.59%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7460\n",
      "Total Return: 19.24%\n",
      "Sortino: 8.71\n",
      "Max Drawdown: -1.02%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 9.39%\n",
      "Sortino: 2.78\n",
      "Max Drawdown: -3.35%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4444\n",
      "Total Return: -1.75%\n",
      "Sortino: -1.13\n",
      "Max Drawdown: -7.60%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 13.24%\n",
      "Sortino: 4.70\n",
      "Max Drawdown: -1.78%\n",
      "Number of Trades: 15\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 8.46%\n",
      "Sortino: 3.30\n",
      "Max Drawdown: -2.48%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 16.30%\n",
      "Sortino: 7.05\n",
      "Max Drawdown: -1.35%\n",
      "Number of Trades: 23\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6032\n",
      "Total Return: 10.12%\n",
      "Sortino: 4.12\n",
      "Max Drawdown: -2.20%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8615\n",
      "Total Return: 32.16%\n",
      "Sortino: 22.07\n",
      "Max Drawdown: -1.37%\n",
      "Number of Trades: 30\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8254\n",
      "Total Return: 25.73%\n",
      "Sortino: 12.76\n",
      "Max Drawdown: -0.99%\n",
      "Number of Trades: 32\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 11.47%\n",
      "Sortino: 3.63\n",
      "Max Drawdown: -2.33%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5238\n",
      "Total Return: -1.18%\n",
      "Sortino: -0.77\n",
      "Max Drawdown: -6.41%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6000\n",
      "Total Return: 11.04%\n",
      "Sortino: 3.75\n",
      "Max Drawdown: -2.64%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 5.14%\n",
      "Sortino: 1.77\n",
      "Max Drawdown: -4.47%\n",
      "Number of Trades: 37\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 14.21%\n",
      "Sortino: 4.95\n",
      "Max Drawdown: -1.93%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 6.97%\n",
      "Sortino: 2.58\n",
      "Max Drawdown: -2.43%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 7.78%\n",
      "Sortino: 2.74\n",
      "Max Drawdown: -3.17%\n",
      "Number of Trades: 9\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4762\n",
      "Total Return: -0.93%\n",
      "Sortino: -0.72\n",
      "Max Drawdown: -3.19%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5846\n",
      "Total Return: 10.53%\n",
      "Sortino: 4.12\n",
      "Max Drawdown: -2.80%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4603\n",
      "Total Return: -0.99%\n",
      "Sortino: -0.76\n",
      "Max Drawdown: -4.50%\n",
      "Number of Trades: 27\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6923\n",
      "Total Return: 21.48%\n",
      "Sortino: 10.65\n",
      "Max Drawdown: -1.48%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6667\n",
      "Total Return: 14.73%\n",
      "Sortino: 6.31\n",
      "Max Drawdown: -1.30%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7846\n",
      "Total Return: 30.17%\n",
      "Sortino: 24.92\n",
      "Max Drawdown: -0.53%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8413\n",
      "Total Return: 25.57%\n",
      "Sortino: 10.67\n",
      "Max Drawdown: -1.02%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 5.32%\n",
      "Sortino: 1.49\n",
      "Max Drawdown: -2.33%\n",
      "Number of Trades: 11\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5556\n",
      "Total Return: 1.63%\n",
      "Sortino: 0.32\n",
      "Max Drawdown: -3.92%\n",
      "Number of Trades: 19\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: 1.23%\n",
      "Sortino: 0.15\n",
      "Max Drawdown: -4.90%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4921\n",
      "Total Return: -3.12%\n",
      "Sortino: -1.45\n",
      "Max Drawdown: -6.84%\n",
      "Number of Trades: 27\n",
      "\n",
      "Analyzing CHFUSD=X_2023\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (260, 6)\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (65, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (260, 9)\n",
      "Enhanced features shape before cleaning: (260, 9)\n",
      "Features shape after cleaning: (259, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 364\n",
      "Test set length (25%): 91 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2023-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-04-01\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 8.60%\n",
      "Sortino: 5.00\n",
      "Max Drawdown: -2.11%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -7.02%\n",
      "Sortino: -4.33\n",
      "Max Drawdown: -6.36%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6308\n",
      "Total Return: 11.50%\n",
      "Sortino: 9.20\n",
      "Max Drawdown: -1.13%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7143\n",
      "Total Return: 12.42%\n",
      "Sortino: 11.99\n",
      "Max Drawdown: -0.97%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -3.12%\n",
      "Sortino: -1.90\n",
      "Max Drawdown: -5.91%\n",
      "Number of Trades: 25\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5873\n",
      "Total Return: 1.06%\n",
      "Sortino: 0.10\n",
      "Max Drawdown: -2.12%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 2.11%\n",
      "Sortino: 0.64\n",
      "Max Drawdown: -2.41%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 8.37%\n",
      "Sortino: 5.88\n",
      "Max Drawdown: -1.37%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6769\n",
      "Total Return: 5.85%\n",
      "Sortino: 2.52\n",
      "Max Drawdown: -2.06%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 8.47%\n",
      "Sortino: 5.76\n",
      "Max Drawdown: -1.51%\n",
      "Number of Trades: 22\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 14.41%\n",
      "Sortino: 10.05\n",
      "Max Drawdown: -0.93%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6825\n",
      "Total Return: 12.05%\n",
      "Sortino: 11.83\n",
      "Max Drawdown: -0.65%\n",
      "Number of Trades: 23\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 1.94%\n",
      "Sortino: 0.58\n",
      "Max Drawdown: -3.10%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 8.19%\n",
      "Sortino: 6.05\n",
      "Max Drawdown: -1.07%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6462\n",
      "Total Return: 4.56%\n",
      "Sortino: 1.79\n",
      "Max Drawdown: -2.41%\n",
      "Number of Trades: 29\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 3.49%\n",
      "Sortino: 1.59\n",
      "Max Drawdown: -2.75%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -0.12%\n",
      "Sortino: -0.58\n",
      "Max Drawdown: -5.32%\n",
      "Number of Trades: 16\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6349\n",
      "Total Return: 7.25%\n",
      "Sortino: 4.80\n",
      "Max Drawdown: -1.39%\n",
      "Number of Trades: 6\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: -0.69%\n",
      "Sortino: -0.75\n",
      "Max Drawdown: -2.76%\n",
      "Number of Trades: 27\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 3.72%\n",
      "Sortino: 1.47\n",
      "Max Drawdown: -2.15%\n",
      "Number of Trades: 20\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5077\n",
      "Total Return: -3.92%\n",
      "Sortino: -2.25\n",
      "Max Drawdown: -4.75%\n",
      "Number of Trades: 17\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7302\n",
      "Total Return: 8.48%\n",
      "Sortino: 5.26\n",
      "Max Drawdown: -1.61%\n",
      "Number of Trades: 28\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 7.59%\n",
      "Sortino: 3.74\n",
      "Max Drawdown: -2.65%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6508\n",
      "Total Return: 10.42%\n",
      "Sortino: 8.16\n",
      "Max Drawdown: -1.08%\n",
      "Number of Trades: 18\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7231\n",
      "Total Return: 12.33%\n",
      "Sortino: 6.78\n",
      "Max Drawdown: -1.30%\n",
      "Number of Trades: 31\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6984\n",
      "Total Return: 11.16%\n",
      "Sortino: 8.78\n",
      "Max Drawdown: -0.88%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -4.78%\n",
      "Sortino: -2.84\n",
      "Max Drawdown: -5.37%\n",
      "Number of Trades: 19\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 7.40%\n",
      "Sortino: 5.33\n",
      "Max Drawdown: -1.10%\n",
      "Number of Trades: 26\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -7.20%\n",
      "Sortino: -4.10\n",
      "Max Drawdown: -7.51%\n",
      "Number of Trades: 17\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6190\n",
      "Total Return: 3.69%\n",
      "Sortino: 1.89\n",
      "Max Drawdown: -2.45%\n",
      "Number of Trades: 24\n",
      "\n",
      "Analyzing CHFUSD=X_2018-19\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2019-12-31\n",
      "Future Testing Period: 2020-01-01 to 2020-07-01\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: -0.21%\n",
      "Sortino: -0.77\n",
      "Max Drawdown: -3.09%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: 2.10%\n",
      "Sortino: 0.06\n",
      "Max Drawdown: -6.20%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8615\n",
      "Total Return: 32.39%\n",
      "Sortino: 18.54\n",
      "Max Drawdown: -0.58%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8125\n",
      "Total Return: 40.04%\n",
      "Sortino: 14.73\n",
      "Max Drawdown: -1.18%\n",
      "Number of Trades: 53\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6692\n",
      "Total Return: 15.66%\n",
      "Sortino: 5.19\n",
      "Max Drawdown: -1.33%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6484\n",
      "Total Return: 13.56%\n",
      "Sortino: 3.24\n",
      "Max Drawdown: -2.21%\n",
      "Number of Trades: 60\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 24.41%\n",
      "Sortino: 10.04\n",
      "Max Drawdown: -0.89%\n",
      "Number of Trades: 54\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5469\n",
      "Total Return: 2.87%\n",
      "Sortino: 0.26\n",
      "Max Drawdown: -3.55%\n",
      "Number of Trades: 60\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7077\n",
      "Total Return: 18.55%\n",
      "Sortino: 5.86\n",
      "Max Drawdown: -2.44%\n",
      "Number of Trades: 68\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5625\n",
      "Total Return: 2.72%\n",
      "Sortino: 0.21\n",
      "Max Drawdown: -6.62%\n",
      "Number of Trades: 65\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8462\n",
      "Total Return: 31.74%\n",
      "Sortino: 16.35\n",
      "Max Drawdown: -0.89%\n",
      "Number of Trades: 63\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7812\n",
      "Total Return: 44.84%\n",
      "Sortino: 27.24\n",
      "Max Drawdown: -0.43%\n",
      "Number of Trades: 53\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6077\n",
      "Total Return: 9.34%\n",
      "Sortino: 2.54\n",
      "Max Drawdown: -2.74%\n",
      "Number of Trades: 62\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 5.34%\n",
      "Sortino: 1.00\n",
      "Max Drawdown: -6.18%\n",
      "Number of Trades: 57\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 19.36%\n",
      "Sortino: 5.99\n",
      "Max Drawdown: -3.02%\n",
      "Number of Trades: 73\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6406\n",
      "Total Return: 20.90%\n",
      "Sortino: 5.78\n",
      "Max Drawdown: -2.18%\n",
      "Number of Trades: 56\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7538\n",
      "Total Return: 25.75%\n",
      "Sortino: 13.56\n",
      "Max Drawdown: -0.56%\n",
      "Number of Trades: 71\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7656\n",
      "Total Return: 25.18%\n",
      "Sortino: 5.20\n",
      "Max Drawdown: -2.94%\n",
      "Number of Trades: 53\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 15.23%\n",
      "Sortino: 5.30\n",
      "Max Drawdown: -1.30%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: -0.57%\n",
      "Sortino: -0.53\n",
      "Max Drawdown: -5.07%\n",
      "Number of Trades: 17\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6538\n",
      "Total Return: 17.83%\n",
      "Sortino: 6.71\n",
      "Max Drawdown: -1.13%\n",
      "Number of Trades: 56\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: 0.79%\n",
      "Sortino: -0.24\n",
      "Max Drawdown: -4.25%\n",
      "Number of Trades: 49\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7308\n",
      "Total Return: 21.08%\n",
      "Sortino: 7.79\n",
      "Max Drawdown: -1.24%\n",
      "Number of Trades: 75\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7266\n",
      "Total Return: 27.90%\n",
      "Sortino: 8.52\n",
      "Max Drawdown: -1.04%\n",
      "Number of Trades: 64\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8154\n",
      "Total Return: 28.20%\n",
      "Sortino: 10.22\n",
      "Max Drawdown: -1.11%\n",
      "Number of Trades: 53\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7969\n",
      "Total Return: 43.80%\n",
      "Sortino: 25.23\n",
      "Max Drawdown: -0.57%\n",
      "Number of Trades: 51\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5231\n",
      "Total Return: -0.71%\n",
      "Sortino: -0.85\n",
      "Max Drawdown: -5.56%\n",
      "Number of Trades: 50\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4922\n",
      "Total Return: -5.93%\n",
      "Sortino: -1.79\n",
      "Max Drawdown: -8.35%\n",
      "Number of Trades: 33\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6846\n",
      "Total Return: 13.36%\n",
      "Sortino: 3.86\n",
      "Max Drawdown: -2.39%\n",
      "Number of Trades: 65\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4922\n",
      "Total Return: -9.68%\n",
      "Sortino: -2.86\n",
      "Max Drawdown: -11.41%\n",
      "Number of Trades: 61\n",
      "\n",
      "Analyzing CHFUSD=X_2020-21\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (522, 6)\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (522, 9)\n",
      "Enhanced features shape before cleaning: (522, 9)\n",
      "Features shape after cleaning: (521, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 730\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2020-01-01 to 2021-12-31\n",
      "Future Testing Period: 2022-01-01 to 2022-07-02\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 0.88%\n",
      "Sortino: -0.36\n",
      "Max Drawdown: -3.36%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -3.76%\n",
      "Sortino: -1.54\n",
      "Max Drawdown: -9.17%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8000\n",
      "Total Return: 31.06%\n",
      "Sortino: 14.53\n",
      "Max Drawdown: -0.59%\n",
      "Number of Trades: 68\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7734\n",
      "Total Return: 42.05%\n",
      "Sortino: 15.69\n",
      "Max Drawdown: -0.97%\n",
      "Number of Trades: 53\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5692\n",
      "Total Return: 6.44%\n",
      "Sortino: 1.55\n",
      "Max Drawdown: -2.48%\n",
      "Number of Trades: 62\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5938\n",
      "Total Return: 11.05%\n",
      "Sortino: 2.32\n",
      "Max Drawdown: -6.31%\n",
      "Number of Trades: 57\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5154\n",
      "Total Return: -0.04%\n",
      "Sortino: -0.60\n",
      "Max Drawdown: -3.77%\n",
      "Number of Trades: 56\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5703\n",
      "Total Return: 8.03%\n",
      "Sortino: 1.57\n",
      "Max Drawdown: -2.59%\n",
      "Number of Trades: 48\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 2.59%\n",
      "Sortino: 0.21\n",
      "Max Drawdown: -3.96%\n",
      "Number of Trades: 61\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5781\n",
      "Total Return: 11.16%\n",
      "Sortino: 2.38\n",
      "Max Drawdown: -4.68%\n",
      "Number of Trades: 63\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7538\n",
      "Total Return: 28.85%\n",
      "Sortino: 13.81\n",
      "Max Drawdown: -0.59%\n",
      "Number of Trades: 64\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7969\n",
      "Total Return: 43.23%\n",
      "Sortino: 16.62\n",
      "Max Drawdown: -0.87%\n",
      "Number of Trades: 63\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 1.40%\n",
      "Sortino: -0.16\n",
      "Max Drawdown: -4.49%\n",
      "Number of Trades: 57\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: -0.37%\n",
      "Sortino: -0.45\n",
      "Max Drawdown: -5.22%\n",
      "Number of Trades: 49\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6154\n",
      "Total Return: 13.17%\n",
      "Sortino: 4.10\n",
      "Max Drawdown: -3.28%\n",
      "Number of Trades: 61\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6641\n",
      "Total Return: 18.07%\n",
      "Sortino: 4.05\n",
      "Max Drawdown: -3.10%\n",
      "Number of Trades: 63\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 5.45%\n",
      "Sortino: 1.16\n",
      "Max Drawdown: -3.63%\n",
      "Number of Trades: 58\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5938\n",
      "Total Return: 14.08%\n",
      "Sortino: 2.57\n",
      "Max Drawdown: -5.15%\n",
      "Number of Trades: 37\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4923\n",
      "Total Return: -2.01%\n",
      "Sortino: -1.24\n",
      "Max Drawdown: -6.49%\n",
      "Number of Trades: 50\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5156\n",
      "Total Return: 5.28%\n",
      "Sortino: 0.90\n",
      "Max Drawdown: -3.09%\n",
      "Number of Trades: 52\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5154\n",
      "Total Return: 1.31%\n",
      "Sortino: -0.19\n",
      "Max Drawdown: -3.61%\n",
      "Number of Trades: 52\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5312\n",
      "Total Return: 8.29%\n",
      "Sortino: 1.78\n",
      "Max Drawdown: -2.27%\n",
      "Number of Trades: 58\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6615\n",
      "Total Return: 16.12%\n",
      "Sortino: 5.06\n",
      "Max Drawdown: -2.71%\n",
      "Number of Trades: 63\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6641\n",
      "Total Return: 22.81%\n",
      "Sortino: 5.57\n",
      "Max Drawdown: -1.91%\n",
      "Number of Trades: 55\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7385\n",
      "Total Return: 26.67%\n",
      "Sortino: 11.80\n",
      "Max Drawdown: -0.69%\n",
      "Number of Trades: 60\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7422\n",
      "Total Return: 38.44%\n",
      "Sortino: 14.00\n",
      "Max Drawdown: -1.01%\n",
      "Number of Trades: 71\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -10.24%\n",
      "Sortino: -3.43\n",
      "Max Drawdown: -11.25%\n",
      "Number of Trades: 56\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4922\n",
      "Total Return: -3.30%\n",
      "Sortino: -1.08\n",
      "Max Drawdown: -4.11%\n",
      "Number of Trades: 42\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5769\n",
      "Total Return: 2.61%\n",
      "Sortino: 0.21\n",
      "Max Drawdown: -2.90%\n",
      "Number of Trades: 57\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6328\n",
      "Total Return: 15.58%\n",
      "Sortino: 3.35\n",
      "Max Drawdown: -2.09%\n",
      "Number of Trades: 54\n",
      "\n",
      "Analyzing CHFUSD=X_2022-23\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (520, 6)\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (130, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (520, 9)\n",
      "Enhanced features shape before cleaning: (520, 9)\n",
      "Features shape after cleaning: (519, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 729\n",
      "Test set length (25%): 182 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2022-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2024-07-01\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 6.74%\n",
      "Sortino: 1.50\n",
      "Max Drawdown: -6.88%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -6.11%\n",
      "Sortino: -2.29\n",
      "Max Drawdown: -7.79%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7000\n",
      "Total Return: 28.59%\n",
      "Sortino: 10.33\n",
      "Max Drawdown: -1.06%\n",
      "Number of Trades: 47\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7422\n",
      "Total Return: 28.71%\n",
      "Sortino: 13.79\n",
      "Max Drawdown: -0.66%\n",
      "Number of Trades: 47\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5462\n",
      "Total Return: 5.39%\n",
      "Sortino: 1.00\n",
      "Max Drawdown: -2.36%\n",
      "Number of Trades: 71\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6562\n",
      "Total Return: 9.70%\n",
      "Sortino: 2.41\n",
      "Max Drawdown: -1.30%\n",
      "Number of Trades: 66\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -0.84%\n",
      "Sortino: -0.77\n",
      "Max Drawdown: -4.93%\n",
      "Number of Trades: 55\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6250\n",
      "Total Return: 8.88%\n",
      "Sortino: 2.26\n",
      "Max Drawdown: -1.69%\n",
      "Number of Trades: 54\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: 1.92%\n",
      "Sortino: 0.01\n",
      "Max Drawdown: -5.32%\n",
      "Number of Trades: 26\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5547\n",
      "Total Return: 9.80%\n",
      "Sortino: 2.89\n",
      "Max Drawdown: -1.51%\n",
      "Number of Trades: 31\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7923\n",
      "Total Return: 35.11%\n",
      "Sortino: 14.47\n",
      "Max Drawdown: -1.37%\n",
      "Number of Trades: 71\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7734\n",
      "Total Return: 29.59%\n",
      "Sortino: 15.00\n",
      "Max Drawdown: -0.66%\n",
      "Number of Trades: 81\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4462\n",
      "Total Return: -6.56%\n",
      "Sortino: -2.26\n",
      "Max Drawdown: -8.29%\n",
      "Number of Trades: 21\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5078\n",
      "Total Return: 5.51%\n",
      "Sortino: 1.28\n",
      "Max Drawdown: -4.71%\n",
      "Number of Trades: 21\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5308\n",
      "Total Return: 1.31%\n",
      "Sortino: -0.16\n",
      "Max Drawdown: -4.93%\n",
      "Number of Trades: 63\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6172\n",
      "Total Return: 12.04%\n",
      "Sortino: 3.82\n",
      "Max Drawdown: -1.57%\n",
      "Number of Trades: 69\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5385\n",
      "Total Return: 9.07%\n",
      "Sortino: 2.25\n",
      "Max Drawdown: -2.40%\n",
      "Number of Trades: 24\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6328\n",
      "Total Return: 17.41%\n",
      "Sortino: 6.20\n",
      "Max Drawdown: -1.13%\n",
      "Number of Trades: 15\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5154\n",
      "Total Return: -4.44%\n",
      "Sortino: -1.65\n",
      "Max Drawdown: -7.35%\n",
      "Number of Trades: 33\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5938\n",
      "Total Return: 5.24%\n",
      "Sortino: 1.07\n",
      "Max Drawdown: -4.43%\n",
      "Number of Trades: 39\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -5.19%\n",
      "Sortino: -1.87\n",
      "Max Drawdown: -7.65%\n",
      "Number of Trades: 37\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6016\n",
      "Total Return: 4.86%\n",
      "Sortino: 0.94\n",
      "Max Drawdown: -4.78%\n",
      "Number of Trades: 45\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5538\n",
      "Total Return: 2.69%\n",
      "Sortino: 0.21\n",
      "Max Drawdown: -4.16%\n",
      "Number of Trades: 59\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.6875\n",
      "Total Return: 14.48%\n",
      "Sortino: 4.64\n",
      "Max Drawdown: -1.19%\n",
      "Number of Trades: 76\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7308\n",
      "Total Return: 30.16%\n",
      "Sortino: 10.99\n",
      "Max Drawdown: -1.09%\n",
      "Number of Trades: 63\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7188\n",
      "Total Return: 25.34%\n",
      "Sortino: 9.66\n",
      "Max Drawdown: -1.13%\n",
      "Number of Trades: 67\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.4846\n",
      "Total Return: -6.74%\n",
      "Sortino: -2.21\n",
      "Max Drawdown: -8.72%\n",
      "Number of Trades: 9\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5781\n",
      "Total Return: 7.27%\n",
      "Sortino: 1.72\n",
      "Max Drawdown: -3.25%\n",
      "Number of Trades: 5\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5308\n",
      "Total Return: -5.99%\n",
      "Sortino: -2.06\n",
      "Max Drawdown: -7.38%\n",
      "Number of Trades: 51\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5859\n",
      "Total Return: 0.20%\n",
      "Sortino: -0.50\n",
      "Max Drawdown: -4.24%\n",
      "Number of Trades: 66\n",
      "\n",
      "Analyzing CHFUSD=X_2018-23\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded training data shape: (1564, 6)\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "Downloaded future data shape: (270, 6)\n",
      "\n",
      "Preparing features...\n",
      "Base features shape before cleaning: (1564, 9)\n",
      "Enhanced features shape before cleaning: (1564, 9)\n",
      "Features shape after cleaning: (1563, 9)\n",
      "\n",
      "Period Calculations:\n",
      "Training period total days: 2190\n",
      "Test set length (25%): 547 days\n",
      "\n",
      "Analysis Periods:\n",
      "Training Period: 2018-01-01 to 2023-12-31\n",
      "Future Testing Period: 2024-01-01 to 2025-07-01\n",
      "\n",
      "Downloading training data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Downloading future test data for CHFUSD=X...\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "\n",
      "Preparing features...\n",
      "\n",
      "Calculating Buy-and-Hold benchmarks...\n",
      "\n",
      "Buy-and-Hold Test Period:\n",
      "Total Return: 13.28%\n",
      "Sortino: 0.58\n",
      "Max Drawdown: -7.11%\n",
      "\n",
      "Buy-and-Hold Future Period:\n",
      "Total Return: -7.69%\n",
      "Sortino: -1.56\n",
      "Max Drawdown: -7.85%\n",
      "\n",
      "Training and evaluating Logistic_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8440\n",
      "Total Return: 262.89%\n",
      "Sortino: 19.55\n",
      "Max Drawdown: -1.22%\n",
      "Number of Trades: 192\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8321\n",
      "Total Return: 104.67%\n",
      "Sortino: 24.13\n",
      "Max Drawdown: -0.42%\n",
      "Number of Trades: 127\n",
      "\n",
      "Training and evaluating DecisionTree_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6854\n",
      "Total Return: 107.25%\n",
      "Sortino: 6.05\n",
      "Max Drawdown: -4.21%\n",
      "Number of Trades: 154\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5261\n",
      "Total Return: 8.42%\n",
      "Sortino: 0.59\n",
      "Max Drawdown: -4.77%\n",
      "Number of Trades: 70\n",
      "\n",
      "Training and evaluating RandomForest_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6650\n",
      "Total Return: 69.94%\n",
      "Sortino: 3.76\n",
      "Max Drawdown: -3.94%\n",
      "Number of Trades: 195\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5634\n",
      "Total Return: 15.36%\n",
      "Sortino: 1.47\n",
      "Max Drawdown: -3.54%\n",
      "Number of Trades: 120\n",
      "\n",
      "Training and evaluating GradientBoosting_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6471\n",
      "Total Return: 79.84%\n",
      "Sortino: 4.50\n",
      "Max Drawdown: -4.28%\n",
      "Number of Trades: 166\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5224\n",
      "Total Return: 9.06%\n",
      "Sortino: 0.69\n",
      "Max Drawdown: -6.72%\n",
      "Number of Trades: 86\n",
      "\n",
      "Training and evaluating Neural_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8389\n",
      "Total Return: 262.96%\n",
      "Sortino: 19.93\n",
      "Max Drawdown: -1.22%\n",
      "Number of Trades: 196\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8321\n",
      "Total Return: 102.80%\n",
      "Sortino: 24.13\n",
      "Max Drawdown: -0.41%\n",
      "Number of Trades: 136\n",
      "\n",
      "Training and evaluating AdaBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5217\n",
      "Total Return: 10.69%\n",
      "Sortino: 0.36\n",
      "Max Drawdown: -9.59%\n",
      "Number of Trades: 122\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4851\n",
      "Total Return: 3.32%\n",
      "Sortino: -0.09\n",
      "Max Drawdown: -5.43%\n",
      "Number of Trades: 36\n",
      "\n",
      "Training and evaluating XGBoost_Base...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6957\n",
      "Total Return: 120.08%\n",
      "Sortino: 6.80\n",
      "Max Drawdown: -4.09%\n",
      "Number of Trades: 183\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5373\n",
      "Total Return: 8.71%\n",
      "Sortino: 0.64\n",
      "Max Drawdown: -6.88%\n",
      "Number of Trades: 110\n",
      "\n",
      "Training and evaluating Logistic_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8312\n",
      "Total Return: 254.73%\n",
      "Sortino: 18.34\n",
      "Max Drawdown: -1.09%\n",
      "Number of Trades: 194\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.7761\n",
      "Total Return: 90.98%\n",
      "Sortino: 17.67\n",
      "Max Drawdown: -1.24%\n",
      "Number of Trades: 127\n",
      "\n",
      "Training and evaluating DecisionTree_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5345\n",
      "Total Return: -5.15%\n",
      "Sortino: -0.74\n",
      "Max Drawdown: -15.50%\n",
      "Number of Trades: 13\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5485\n",
      "Total Return: 7.72%\n",
      "Sortino: 0.48\n",
      "Max Drawdown: -8.81%\n",
      "Number of Trades: 1\n",
      "\n",
      "Training and evaluating RandomForest_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.6496\n",
      "Total Return: 71.68%\n",
      "Sortino: 4.03\n",
      "Max Drawdown: -7.15%\n",
      "Number of Trades: 181\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5672\n",
      "Total Return: 9.63%\n",
      "Sortino: 0.72\n",
      "Max Drawdown: -7.45%\n",
      "Number of Trades: 112\n",
      "\n",
      "Training and evaluating GradientBoosting_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.7315\n",
      "Total Return: 156.80%\n",
      "Sortino: 9.99\n",
      "Max Drawdown: -2.88%\n",
      "Number of Trades: 172\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5522\n",
      "Total Return: 14.48%\n",
      "Sortino: 1.39\n",
      "Max Drawdown: -3.75%\n",
      "Number of Trades: 90\n",
      "\n",
      "Training and evaluating Neural_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.8338\n",
      "Total Return: 265.08%\n",
      "Sortino: 20.30\n",
      "Max Drawdown: -1.01%\n",
      "Number of Trades: 198\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.8172\n",
      "Total Return: 94.67%\n",
      "Sortino: 17.52\n",
      "Max Drawdown: -0.84%\n",
      "Number of Trades: 128\n",
      "\n",
      "Training and evaluating AdaBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5166\n",
      "Total Return: -7.65%\n",
      "Sortino: -0.92\n",
      "Max Drawdown: -18.64%\n",
      "Number of Trades: 84\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.5000\n",
      "Total Return: -1.40%\n",
      "Sortino: -0.72\n",
      "Max Drawdown: -6.04%\n",
      "Number of Trades: 50\n",
      "\n",
      "Training and evaluating XGBoost_Enhanced...\n",
      "\n",
      "Test Period Results:\n",
      "Accuracy: 0.5729\n",
      "Total Return: 27.67%\n",
      "Sortino: 1.55\n",
      "Max Drawdown: -10.24%\n",
      "Number of Trades: 181\n",
      "\n",
      "Future Period Results:\n",
      "Accuracy: 0.4515\n",
      "Total Return: -13.71%\n",
      "Sortino: -2.49\n",
      "Max Drawdown: -17.09%\n",
      "Number of Trades: 119\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run the analysis\n",
    "all_results = multi_period_analysis()\n",
    "average_metrics = calculate_aggregate_metrics(all_results)\n",
    "plot_aggregate_comparisons(average_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b5be160-8141-4ca9-9d58-c23f74cccf8d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6ca8aaf-afe5-499e-93bc-ac5c9a13570f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66aad1cb-7241-48ba-b625-b7ba96876960",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1a3c9a4-e9e8-4e9b-92e4-e3ca273143b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f07eb94d-0197-4df4-a220-bab32771be72",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
